Methods and kit for characterizing the modified base status of a transcriptome

ABSTRACT

This invention relates to a method of characterizing the modified base status of a transcriptome, which involves contacting a transcriptome comprising one or more modified bases with an antibody specific to the modified bases under conditions effective to bind the antibody to the modified bases; isolating, from the transcriptome, a pool of RNA transcripts to which the antibody binds; and identifying isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome, where each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome. Also disclosed are a method of diagnosis or prognosis of a disease, a method of determining the effect of a treatment on modified base levels in RNA, and a kit for characterizing the modified base status of a transcriptome.

This application claims the priority benefit of U.S. Provisional Patent Application Ser. No. 61/599,714, filed Feb. 16, 2012, which is hereby incorporated by reference in its entirety.

The subject matter of this application was made with support from the United States Government under NIMH R01 080420 under the National Institute of Mental Health. The United States Government has certain rights.

FIELD OF THE INVENTION

This invention relates to methods and a kit for characterizing the modified base status of a transcriptome.

BACKGROUND OF THE INVENTION

The distribution of modified bases in RNA is poorly understood. For example, previous studies have found that the base modification of N⁶-methyladenosine (“m⁶A”) exists in RNA in a variety of organisms, including viruses, yeast, and mammals (Beemon et al., “Localization of N6-Methyladenosine in the Rous Sarcoma Virus Genome,” J. Mol. Biol. 113:165-179 (1977); Bodi et al., “Yeast Targets for mRNA Methylation,” Nucleic Acids Res. 38:5327-5335 (2010)). Moreover, m⁶A is found in tRNA (Saneyoshi et al., “Isolation and Characterization of N6-Methyladenosine from Escherichia coli Valine Transfer RNA,” Biochim. Biophys. Acta. 190:264-273 (1969)), rRNA (Iwanami et al., “Methylated Bases of Ribosomal Ribonucleic Acid from HeLa Cells,” Arch. Biochem. Biophys. 126:8-15 (1968)), and viral RNA (Beemon et al., “Localization of N6-Methyladenosine in the Rous Sarcoma Virus Genome,” J. Mol. Biol. 113:165-179 (1977); Dimock et al., “Sequence Specificity of Internal Methylation in B77 Avian Sarcoma Virus RNA Subunits,” Biochemistry 16:471-478 (1977)). However, while m⁶A is detectable in mRNA-enriched RNA fractions (Desrosiers et al., “Identification of Methylated Nucleosides in Messenger RNA from Novikoff Hepatoma Cells,” Proc. Natl. Acad. Sci. U.S.A. 71:3971-3975 (1974)), it has been confirmed in vivo in only one mammalian mRNA (Horowitz et al., “Mapping of N6-Methyladenosine Residues in Bovine Prolactin mRNA,” Proc. Natl. Acad. Sci. U.S.A. 81:5667-5671 (1984)). Preliminary experiments showed that m⁶A appeared to be enriched in the nucleus, indicating that it may be added before splicing (Chen-Kiang et al., “N-6-methyl-adenosine in Adenovirus Type 2 Nuclear RNA is Conserved in the Formation of Messenger RNA,” J. Mol. Biol. 135:733-752 (1979)), but this was only a speculation about which transcripts were affected. It was not known if m⁶A is found on just a few transcripts, subset of transcripts, or on all transcripts, and it was not know if the m⁶A is randomly distributed within any given transcript or if it has unique localizations in mRNA.

Moreover, the abundance of m⁶A has been shown to be 0.1-0.4% of total adenosine residues in cellular RNA (Dubin et al., “The Methylation State of Poly A-Containing Messenger RNA from Cultured Hamster Cells,” Nucleic Acids Res. 2:1653-1668 (1975); Perry et al., “The Methylated Constituents of L Cell Messenger RNA: Evidence for an Unusual Cluster at the 5′ Terminus,” Cell 4:387-394 (1975); Wei et al., “Methylated Nucleotides Block 5′ Terminus of HeLa Cell Messenger RNA,” Cell 4:379-386 (1975)), indicating that this modification may be widespread throughout the transcriptome.

Although the existence of modified bases in RNAs is known, transcriptome-wide characterization of modified bases in RNA has not previously been accomplished. This is due, in part, to the lack of available methods for detecting the presence of modified bases in RNA. Moreover, certain base modifications, such as m⁶A, do not alter their ability to base pair with thymidine or uracil, so they are not amenable to detection with standard hybridization or sequencing-based methods.

The present invention is directed to overcoming these and other limitations in the art.

SUMMARY OF THE INVENTION

A first aspect of the present invention relates to a method of characterizing the modified base status of a transcriptome. This method involves contacting a transcriptome comprising one or more modified bases with an antibody specific to the one or more modified bases under conditions effective to bind the antibody to the one or more modified bases; isolating, from the transcriptome, a pool of RNA transcripts to which the antibody binds; and identifying isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome, where each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome.

A second aspect of the present invention relates to a method of diagnosis or prognosis of a disease or disorder associated with a modified base in RNA in a subject. This method involves obtaining a transcriptome from a subject, where the transcriptome comprises one or more modified bases; characterizing the modified base status of the transcriptome; and comparing the modified base status of the transcriptome to a known modified base status of a comparable transcriptome from a healthy and/or diseased subject to provide a diagnosis or prognosis of a disease or disorder in the subject.

A third aspect of the present invention relates to a method of determining the effect of a treatment on modified base levels of a transcriptome from a subject. This method involves characterizing the modified base status of a transcriptome comprising one or more modified bases from a subject before a treatment is administered to the subject; characterizing the modified base status of the transcriptome from the subject after a treatment is administered to the subject; and comparing the modified base status of the transcriptome after said treatment to the modified base status of the transcriptome before said treatment to determine the effect of the treatment on modified base levels of the transcriptome in the subject.

A fourth aspect of the present invention relates to a kit for characterizing the modified base status of a transcriptome. The kit includes an antibody that binds a modified base in RNA; one or more buffer solutions for carrying out antibody binding to a modified base in RNA, isolation of antibody-bound RNA, and/or sequencing of RNA molecules; and a computer-readable medium having stored thereon computer-readable instructions for comparing the abundance of an RNA transcript fragment in an isolated pool of RNA transcripts from a transcriptome to the abundance of that RNA transcript in the transcriptome prior to isolation thereof to identify isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome, where each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome.

The methods and kit of the present invention enable the characterization of the prevalence, regulation, and functional roles of modified bases in the transcriptome. Using antibodies that recognize modified bases in RNA transcripts, an affinity enrichment strategy has been developed which, when coupled with next-generation sequencing, allows for the high-throughput identification of modified base sites. Using this approach, the first transcriptome-wide profile of the modified base m⁶A in RNA is presented. Using the methods of the present invention it was found that m⁶A is a widespread modification that is present in the mRNAs of over 7,600 genes and in over 300 noncoding RNAs. Additionally, m⁶A is highly enriched near the stop codon and in the 3′ UTR. Furthermore, bioinformatic analysis of m⁶A localization reveals consensus sites for m⁶A and identifies a potential interaction between m⁶A and microRNA pathways.

The present invention allows the identification of all the transcripts (coding and non-coding) in a given cell or tissue that contain modified (or substituted) base residues. In carrying out the methods of the present invention, it was found that a significant portion of the cellular transcriptome contains transcripts which contain one or more m⁶A residues. Thus, for example, the present invention allows for identification of transcripts which contain, or do not contain, detectable levels of m⁶A.

The present invention also relates to identifying the sites within transcripts that are likely to contain modified base residues. It is possible that some disease states involve the loss of modified base residues at certain sites and not others. According to the present invention, one can determine the relative amount of any particular modified base in a transcript, relative to the amount of that modified base in the same transcript, but a different tissue. For example, based on a comparison between normal cells and cancer cells, predictions can be made about whether the modified base levels at specific sites within the tubulin transcript have increased or decreased.

The recent discovery that FTO, an obesity risk gene, encodes an m⁶A demethylase implicates m⁶A as an important regulator of physiological processes. The present invention shows that m⁶A is a highly prevalent base modification present in all tested tissues, but that also exhibits tissue-specific regulation and is markedly increased throughout brain development. Using a novel method for transcriptome-wide m⁶A localization, mRNAs of 7,676 genes were identified which contain m⁶A, indicating that m⁶A is a common modification of mRNA. It was found that m⁶A sites are enriched near stop codons and in 3′ UTRs, and an association between the presence of m⁶A residues and microRNA binding sites within 3′ UTRs was uncovered. These findings provide a resource for identifying transcripts that are substrates for modified bases of RNA transcripts and reveal novel insights into the epigenetic regulation of the mammalian transcriptome.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1D demonstrate the specificity and sensitivity of m⁶A-specific antibody. In particular, FIG. 1A depicts a dot blot analysis that demonstrates antibody specificity for m⁶A. Increasing amounts of an oligonucleotide containing either m⁶A or unmodified adenosine were spotted onto a membrane and probed with the m⁶A antibody. While increased m⁶A immunoreactivity is observed in the presence of increasing concentrations of the m⁶A oligonucleotide (top), only background levels of immunoreactivity are observed at the highest concentrations of the A oligonucleotide (bottom). Blots shown are representative of results from three experiments. FIG. 1B shows competition dot blot assays that were performed on membranes spotted with 100 ng of m⁶A-containing oligonucleotide. Antibody binding to the m⁶A oligonucleotide is attenuated by pre-incubation with increasing amounts of m⁶A-containing competitor RNA (top), but not with RNA containing unmodified adenosine (bottom). Amount of competitor RNA used (left to right): 0 ng (0 nM), 10 ng (0.1 nM), 100 ng (1.1 nM), 1 μg (11.2 nM). Blots shown are representative of results from four experiments. In FIG. 1C, competition dot blot assays were performed as in FIG. 1B. The antibody was pre-incubated with increasing amounts of N⁶-methyladenosine triphosphate (N⁶-MeATP), adenosine triphosphate (ATP), N¹-methyladenosine triphosphate (N¹-MeATP), or 2′-O-methyladenosine triphosphate (2′-O-MeATP). Only N⁶-MeATP is able to compete with antibody binding. Concentration of competitor nucleotide used (left to right): 0 μM, 1 μM, 2 μM, 4 μM. Blots shown are representative of results from three experiments. FIG. 1D illustrates detection of m⁶A in cellular DNA. Genomic DNA isolated from dam+ (containing m⁶A) or dam− (lacking m⁶A) E. coli was sheared and subjected to immunoblotting with the anti-m⁶A antibody. Although 1.5 times as much DNA from dam− E. coli was loaded (left panel), the antibody only recognizes the m⁶A present in DNA from dam+ E. coli (right panel). The blot shown is representative of results from three experiments.

FIGS. 2A-2D demonstrate the distribution and dynamic cellular regulation of m⁶A in RNA. FIG. 2A depicts widespread distribution of m⁶A levels in a variety of tissues. Total RNA isolated from mouse brain, heart, lung, liver, and kidney (top) was subjected to m⁶A immunoblot analysis. Ethidium bromide staining of the 28S rRNA is shown as a loading control (bottom). FIG. 2B is a graph showing quantification of m⁶A abundance within various tissues. Quantification of m⁶A immunoreactivity in FIG. 2A was measured by densitometry and normalized to the intensity of the corresponding 28S rRNA band for each tissue (n=3; data are presented as mean±SEM). FIG. 2C shows m⁶A that is enriched within mRNAs. Oligo(dT) Dynabeads were used to isolate poly(A) RNA from total mouse brain RNA, and the unbound “flow-through” RNA was saved as the poly(A)-depleted fraction. Equal amounts of total RNA, poly(A) RNA, and poly(A)-depleted RNA were then subjected to m⁶A immunoblot analysis (top). Ethidium bromide staining of 28S rRNA is shown as a loading control (bottom). Intense m⁶A immunoreactivity is observed in the poly(A) RNA fraction, consistent with high levels of m⁶A within mature mRNAs. FIG. 2D shows that depletion of poly(A) tails from mRNA does not reduce levels of m⁶A in mRNA. Poly(A) RNA was isolated from total mouse brain RNA using oligo(dT) Dynabeads. Half the sample was then subjected to poly(A) tail depletion by hybridizing to oligo(dT) primers and digestion with RNase H. Immunoblot analysis with the m⁶A antibody (top panel) shows that levels of m⁶A in poly(A) RNA (left) and poly(A) tail-depleted RNA (right) are comparable. Removal of poly(A) tails was confirmed using 3′RACE and RT-PCR to detect β-actin; no product is detected in the tail-depleted sample when oligo(dT) primers are used for cDNA synthesis (middle panel). As a control, use of random hexamers successfully generates a product in both samples (bottom panel).

FIGS. 3A-3B depict regulation of m⁶A levels in cells and during development. FIG. 3A shows ontogeny of m⁶A abundance throughout brain development. Total RNA was isolated from mouse brain at embryonic day 18 (E18), postnatal day 0 (P0), postnatal day 14 (P14), and adulthood (Adult), then subjected to immunoblot analysis to detect m⁶A-containing transcripts. Ethidium bromide staining of 28S rRNA bands is shown as a loading control. FIG. 3B shows that FTO demethylates a wide range of cellular transcripts. FTO was expressed in HEK293T cells for 48 hours, and cellular RNA was subjected to immunoblot analysis to detect m⁶A.

FIGS. 4A-4C show an outline of m⁶A immunoprecipitation/RNA-Seq (MeRIP-Seq) protocol and distribution of sequencing reads. FIG. 4A is a schematic representation of MeRIP-Seq. Total RNA is subjected to RiboMinus treatment to remove rRNA species. RNAs containing m⁶A are then immunoprecipitated by mixing the RNA with m⁶A antibody-coupled Dynabeads. m⁶A-containing RNAs are then eluted from the antibody-coupled beads and subjected to a second round of m⁶A immunoprecipitation. The resulting RNA pool, which is highly enriched for m⁶A-containing RNAs, is then subjected to next-generation sequencing. FIG. 4B shows a schematic of sequencing reads and their alignment to locations in the genome surrounding an m⁶A site. The top figure shows an mRNA that contains a single m⁶A residue along its length. The middle figure shows individual, 100 nt-wide mRNA fragments which are isolated following m⁶A immunoprecipitation, each of which contains the same m⁶A residue from the mRNA depicted above. The bottom figure is a histogram showing predicted frequency of MeRIP-Seq reads obtained by sequencing individual immunoprecipitated fragments. Read frequency is predicted to increase with closer proximity to the m⁶A site, forming a “peak” which is roughly 200 nt wide at its base and 100 nt wide at its midpoint. FIG. 4C shows that sequencing reads from MeRIP-Seq converge over m⁶A sites. Representative UCSC Genome Browser plot from MeRIP-Seq data which demonstrates typical read frequency peak formation surrounding a site of m⁶A (shown here is the 3′ UTR of Pax6). Peak height is displayed as reads per base per million mapped reads (BPM).

FIGS. 5A-5E show validation of m⁶A targets and characteristics of m⁶A localization. FIG. 5A shows that different sequencing platforms and antibodies result in similar m⁶A profiles. UCSC Genome Browser tracks displaying read clusters from three MeRIP-Seq replicates (MeRIP1, MeRIP2, and MeRIP3) are shown along the length of the Ldlr transcript. The upper-most track (non-IP) represents the non-immunoprecipitated control sample. FIG. 5B shows validation of m⁶A-containing mRNA identified with MeRIP-Seq. Hybridization-based RNA pulldown was used to isolate Ldlr mRNA from total brain RNA, followed by confirmation of m⁶A presence (arrow) by immunoblot analysis with anti-m⁶A. A control sample using a non-specific probe of equal size (Control Probe) was run in parallel. Total mouse brain RNA (Input RNA) is shown as a reference for m⁶A labeling. FIG. 5C illustrates transcriptome-wide distribution of m⁶A peaks. Pie charts show the percentage of m⁶A peaks (top) and non-IP sample reads (bottom) within distinct RNA sequence types. m⁶A is highly enriched in 3′ UTRs and coding sequences (“CDSs”) compared to the distribution of reads in the non-IP samples. FIG. 5D is a graph showing the distribution of m⁶A peaks across the length of mRNA transcripts. 5′ UTRs, CDSs, and 3′ UTRs of RefSeq mRNAs were individually binned into regions spanning 1% of their total length, and the percentage of m⁶A peaks that fall within each bin was determined. The moving averages of mouse brain peaks percentage and HEK293T peak percentage are shown. FIG. 5E shows that highly similar m⁶A peak distribution is observed within many human and mouse transcripts. UCSC Genome Browser plots are used to show MeRIP-Seq read clusters in the representative transcript SREK1. MeRIP-Seq reads cluster at the same distinct regions of SREK1 in both HEK293T cell RNA (top) and mouse brain RNA (bottom).

FIGS. 6A-6F show that MeRIP-Seq reveals features of m⁶A in mRNA. FIG. 6A shows phylogenetic conservation of m⁶A peaks. PhyloP scores of m⁶A peak regions were compared to those of randomly shuffled regions throughout gene exons. There was a significantly higher median conservation score (K-S test, * p≦2.2e⁻¹⁶) in m⁶A peaks (0.578) than in the random regions (0.023). FIG. 6B shows sequence motifs identified within m⁶A peaks. The motif G[AG]ACU and variants thereof ([AC]GAC[GU], GGAC, [AU][CG]G[AG]AC, and UGAC) were highly enriched in m⁶A peaks. Additionally, one U-rich motif (bottom right) was identified as being significantly underrepresented within m⁶A peaks. Bars under each motif indicate the degree of underrepresentation (black) or overrepresentation (gray) within regions of m⁶A peaks in the non-IP control sample (CNTL) and the MeRIP sample (MeRIP). FIG. 6C illustrates that m⁶A motif sequences frequently lie near the center of m⁶A peaks. Shown is a plot of the cumulative distribution of m⁶A motif positions within m⁶A peaks containing a single motif. Motifs cluster in the center of peaks, indicating that the methylated adenosines in these motifs account for the m⁶A peaks identified in MeRIP-Seq. FIG. 6D is an example of a m⁶A motif sequence near the center of a peak. A UCSC Genome Browser plot containing tracks for MeRIP-Seq reads (red) and non-IP control reads (black) at the Ilf2 locus is shown. The m⁶A peak within the Ilf2 3′ UTR contains a single m⁶A motif identified in FIG. 6B. The sequence of this motif (highlighted in gray) is located at the center of the m⁶A peak. FIG. 6E shows distribution of m⁶A peaks and miRNA target sites within 3′ UTRs. The frequency of m⁶A peaks and miRNA target sites along the length of 3′ UTRs is shown. FIG. 6F shows the association between 3′ UTR methylation and miRNA abundance. The 25 most abundant miRNAs in brain have a significantly greater percentage of m⁶A peaks within their target mRNA 3′ UTRs than do the 25 most weakly expressed brain miRNAs (*p<0.05, Wilcoxon test).

FIGS. 7A-7E show validation of MeRIP-Seq target mRNAs. FIG. 7A shows that MeRIP-Seq identifies Drd1a as an mRNA containing m⁶A. UCSC Genome Browser tracks displaying read clusters from a MeRIP-Seq sample (bottom) and a non-IP control sample (top). Drd1a exhibits distinct m⁶A peaks in the MeRIP sample, whereas it lacks these peaks in the non-IP sample. Peak height is displayed as reads per base per million mapped reads (BPM). FIG. 7B shows confirmation of the presence of m⁶A in Drd1a, an mRNA identified with MeRIP-Seq. Drd1a mRNA was isolated from total mouse brain RNA using a biotinylated oligonucleotide probe in an RNA pull-down. Immunoblot analysis with the anti-m⁶A antibody was then performed to confirm m⁶A presence in Drd1a. A control sample using a probe of equal size that is not specific for any known mouse mRNA (Control Probe) was run in parallel. Total mouse brain RNA (Input) is also shown as a reference for m⁶A labeling. Arrows indicate m⁶A-immunoreactive band following pull-down of Drd1a. The size of the band is consistent with known molecular weights of Drd1a transcript variants (Thierry-Mieg et al., “AceView: A Comprehensive cDNA-Supported Gene and Transcripts Annotation,” Genome Biol. 7 (Suppl. 1):11-14 (2006), which is hereby incorporated by reference in its entirety). FIG. 7C illustrates that MeRIP-Seq identifies Grm1 as an mRNA m⁶A-containing mRNA. UCSC Genome Browser tracks displaying read clusters from a MeRIP-Seq sample (bottom) and the control sample, which comprised the RNA sample prior to immunoprecipitation with the anti-m⁶A antibody (“non-IP,” top). Grm1 exhibits distinct m⁶A peaks along its length in the MeRIP sample, whereas it lacks these peaks in the non-IP sample. Peak height is displayed as reads per base per million mapped reads (BPM). FIG. 7D shows validation of the presence of m⁶A in the Grm1 mRNA. Grm1 mRNA was isolated from total mouse brain RNA using a target-specific, biotinylated oligonucleotide probe as in FIG. 7B. Immunoblot analysis with anti-m⁶A was subsequently performed to confirm m⁶A presence. Total mouse brain RNA (Input) is also shown, as is the results of a control sample using no probe (No Probe). An m⁶A immunoreactive band is observed following pull-down of Grm1 (arrow). The size of the band is consistent with known molecular weights of Grm1 transcript variants (Thierry-Mieg et al., “AceView: A Comprehensive cDNA-Supported Gene and Transcripts Annotation,” Genome Biol. 7 (Suppl. 1):11-14 (2006), which is hereby incorporated by reference in its entirety). FIG. 7E shows immunodepletion of m⁶A-containing mRNAs from complex RNA samples following m⁶A immunoprecipitation. RiboMinus-treated mouse brain RNA was fragmented and subjected to m⁶A immunoprecipitation. Unbound RNAs were isolated, and the abundance of target RNAs was measured by qRT-PCR. All transcripts were normalized to the amount of Rps14 mRNA within each sample. Rps14 was chosen because it is an abundant transcript which does not have m⁶A peaks. Compared to the input RNA, the levels of Rps21 and Ndel1, two transcripts which lack m⁶A peaks, show only slight decreases in the unbound sample (which might be due to non-specific binding of RNA to the magnetic beads used during immunoprecipitation). However, the levels of Drd1a, Grm1, Ptpn4, and Tlr3, all transcripts which contain m⁶A peaks, are dramatically decreased in the unbound RNA fraction, indicating that the m⁶A antibody selectively immunodepletes these methylated transcripts from the unbound RNA pool.

FIGS. 8A-8D show distribution of m⁶A Peaks in mouse brain and HEK293T cell RNA. FIG. 8A is a graph illustrating that many transcripts contain adjacent m⁶A peaks. The number of transcriptome-wide m⁶A peaks that are contiguous with neighboring m⁶A peaks is shown. Many m⁶A peaks occur singly (1 peak within a cluster), although the majority of peaks are part of adjacent peak pairs (2 peaks within a cluster) or contiguous peak triplets (3 peaks within a cluster). A small number of peaks are highly clustered (4 or more peaks within a cluster). This data demonstrates that some transcripts contain a single region of adenosine methylation, whereas other transcripts are multi-methylated on several adenosine residues which cluster in distinct regions of a transcript. FIG. 8B depicts a number of motifs found in m⁶A peaks. The percentage of m⁶A peaks that contain various numbers of the motifs identified (in FIG. 8B) was determined. Only 10% of m⁶A peaks lack a motif, whereas the majority of peaks (57.3%) contain one or two motifs. 28.5% of m⁶A peaks have only a single motif within their sequence, indicating that a single m⁶A residue accounts for these peaks. FIG. 8C shows the relationship between m⁶A peak enrichment and mRNA abundance in mouse brain. Plotted is the peak enrichment value (the ratio of MeRIP sample reads to non-IP sample reads within the area of a peak, each normalized to the number of reads within the sample) relative to the abundance of the transcript within the input RNA. The RPKM (reads per kilobase per million mapped reads) of mRNAs in the non-IP sample (y-axis) provides an estimate of transcript abundance within the input RNA. The most highly enriched m⁶A peaks are often observed in transcripts of low abundance. FIG. 8D shows the relationship between m⁶A peak enrichment and mRNA abundance in HEK293T cells. The enrichment of individual m⁶A peaks is plotted relative to the abundance of the transcript in which the peak resides as in FIG. 8C. As in the mouse brain dataset, there is a tendency for highly enriched m⁶A peaks to occur in weakly expressed transcripts.

FIGS. 9A-9D illustrate features of adenosine methylation in the mouse and human transcriptomes. FIG. 9A indicates distribution of m⁶A peaks surrounding the CDS start site. The distribution of m⁶A peaks 1 kb upstream and downstream of the CDS start sites of known RefSeq genes is shown. A steady increase in the number of peaks is observed which plateaus approximately 500 nt after the CDS start site. FIG. 9B shows distribution of m⁶A peaks surrounding the CDS end site. The distribution of m⁶A peaks 1 kb upstream and downstream of the CDS end sites of known RefSeq genes is shown. A strong and very distinct enrichment of m⁶A peaks surrounding the stop codon is observed. In FIG. 9C, distribution of m⁶A enrichment along the length of mRNA transcripts in mouse is shown. Peaks that fell within gene exons were mapped to percentile locations within the 5′ UTR, CDS and 3′ UTR segments of the mature transcript. Shown is the sum of the enrichments of the peaks that fell within each percentile bin. m⁶A enrichment is particularly strong at the 3′ end of the CDS and the 5′ end of the 3′ UTR. In FIG. 9D, transcriptome-wide distribution of HEK293T m⁶A peaks is shown. The pie chart of FIG. 9D shows the percentage of m⁶A peaks within distinct RNA sequence types. m⁶A is highly enriched in 3′ UTRs and CDSs, similar to the pattern observed in mouse brain RNA (FIG. 5C).

FIGS. 10A-10B show that m⁶A-containing transcripts are found at varying levels across individual cell lines and are not detected in poly(A) tails. In FIG. 10A, cell line-specific changes in the levels of m⁶A indicate its dynamic nature. RNA was isolated from various mammalian cell lines and subjected to immunoblot analysis with the anti-m⁶A antibody. The levels of m⁶A vary substantially across different cell lines. For instance, the cancer cell lines HEPG2 and MCF7 appear to have relatively high levels of m⁶A, whereas the prostate cancer cell lines PC3 and PC9 have comparatively low levels. m⁶A immunoblotting of total mRNA often reveals two bands of low signal intensity that are approximately 1.9 kb and 5 kb in size, which correspond to the 18S and 28S rRNA species, respectively. This likely indicates low levels of m⁶A within these rRNAs. FIG. 10B shows that developmentally regulated increases in m⁶A abundance are observed in cultured neurons. RNA was collected from cultured neurons isolated from embryonic day 18 (E18) and postnatal day 3 (P3) rat brain and subjected to immunoblot analysis with the anti-m⁶A antibody. A substantial increase in m⁶A abundance is observed in P3 neurons compared to E18 neurons, despite loading of less RNA in the P3 sample (indicated by ethidium bromide (EtBr) staining of 28S rRNA bands, bottom panel). Additionally, compared to RNA isolated from adult rat brain tissue, the m⁶A content of E18 or P3 cultured neuronal RNA is significantly enriched in lower molecular weight RNA species.

FIGS. 11A-11B show that m⁶A levels are altered in human disease tissues. In FIG. 11A, m⁶A is increased in brain RNA from Alzheimer's disease patients. Dot blot analysis was performed using equal amounts of total human brain RNA from five Alzheimer's disease patients and two healthy controls. An anti-m⁶A antibody was used for the detection of m⁶A. m⁶A levels are substantially elevated in Alzheimer's disease samples compared to samples from healthy human brain, indicating that increased adenosine methylation might be a feature of Alzheimer's disease brain mRNA. FIG. 11B shows that m⁶A levels are increased in a cellular model of human cancer. Lymphoma B cells were subjected to heat shock treatment (HS), which induces the expression of cancer-related genes. Total RNA was isolated and subjected to dot blot analysis using an anti-m⁶A antibody for detection of m⁶A levels. Compared to untreated cells (No HS), heat shock treatment increases the level of m⁶A in lymphoma B cells, indicating that elevated m⁶A levels are a potential feature of some human cancers.

FIG. 12 is an exemplary environment comprising a statistical computing device for characterizing a transcriptome according to the presence of modified bases in the RNA transcripts contained in the transcriptome.

FIG. 13 is a flowchart showing a computational algorithm for analysis of RNA-IP data. Raw sequence data (fastq, top) is first taken and a gapped alignment of the reads to the genome is performed, which also estimates spliced and fragmented transcripts, a step called “r-make” that utilizes the parallelized and provenance-tracking tools (including the config.xml and .sh scripts) from UNIX's “make” but programmed to work with RNA data; hence, r-make. These alignments created sorted .bam files that store the alignment of the reads to the genome and transcriptome, which are then fed into the MeRIPPer algorithm, which carries out a window-based approach across the genome to find regions of 25 bp (this value is modular) that are significantly different using Fisher's Exact Test, using the IP′d sample vs. the control. These p-values are then corrected for multiple testing, merged, and peaks are called and ranked for significance and degree of change. If peaks are larger than the known fragment size, they are split into sub-peaks. These peaks may then be annotated, searched for motifs, or further analyzed by other tools like FIRE or Jenotator. These comparisons include the examination of peaks as they change in regards to sequence complexity, splicing level changes, gene expression change, peak location shift, peak intensity shift, motif change, or any other aspect of the sequences' changes, their effect on gene function, or interactions with other gene products.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to characterizing a transcriptome according to the presence of modified bases in the RNA transcripts contained in the transcriptome. Thus, in one aspect of the present invention, a transcriptome is characterized by the presence of modified bases in the RNA transcripts contained in the transcriptome by contacting a transcriptome comprising one or more modified bases with an antibody specific to the one or more modified bases under conditions effective to bind the antibody to the one or more modified bases.

As used herein, the term “transcriptome” refers to all RNA molecules produced in one or a population of cells, or a selection of specific types of RNA molecules (e.g., mRNA vs. ncRNA, or specific mRNAs within an mRNA transcriptome) contained in a complete transcriptome. A person of ordinary skill in the art will appreciate that there are many types of RNA molecules in any given transcriptome, including coding RNA (i.e., RNA that is translated into a protein, e.g., mRNA) and non-coding RNA. Table 1 below provides a listing of the various types of RNA molecules found in a transcriptome, all of which may contain modified bases, and each of which is a type of RNA molecule contemplated by the present invention.

TABLE 1 RNA Types Full Name Abb. Name Functional Description 7SK RNA 7SK Negatively regulating CDK9/ cyclin T complex Signal recognition particle 7SRNA Membrane integration RNA Antisense RNA aRNA Regulatory CRISPR RNA crRNA Resistance to parasites Guide RNA gRNA mRNA nucleotide modification Long noncoding RNA lncRNA XIST (dosage compensation), HOTAIR (cancer) MicroRNA miRNA Gene regulation Messenger RNA mRNA Codes for protein Piwi-interacting RNA piRNA Transposon defense, maybe other functions Repeat associated siRNA rasiRNA Type of piRNA; transposon defense Retrotransposon retroRNA self-propagation Ribonuclease MRP RNase MRP rRNA maturation, DNA replication Ribonuclease P RNase P tRNA maturation Ribosomal RNA rRNA Translation Small Cajal body-specific scaRNA Guide RNA to telomere in RNA active cells Small interfering RNA siRNA Gene regulation SmY RNA SmY mRNA trans-splicing Small nucleolar RNA snoRNA Nucleotide modification of RNAs Small nuclear RNA snRNA Splicing and other functions Trans-acting siRNA tasiRNA Gene regulation Telomerase RNA telRNA Telomere synthesis Transfer-messenger RNA tmRNA Rescuing stalled ribosomes Transfer RNA tRNA Translation Viral Response RNA viRNA Anti-viral immunity Vault RNA vRNA self-propagation Y RNA yRNA RNA processing, DNA replication

As used herein, a “modified base” is, according to one embodiment, a ribonucleotide base of uracil, cytosine, adenine, or guanine that possesses a chemical modification from its normal structure. For example, one type of modified base is a methylated base, such as N⁶-methyladenosine (m⁶A). According to another embodiment, a modified base according to the present invention is a substituted base, meaning the base possesses a structural modification that renders it a chemical entity other than uracil, cytosine, adenine, or guanine. For example, pseudouridine is one type of substituted RNA base. Modified bases of the present invention include those now known, and those yet to be discovered. This method of the present invention is applicable to many other types of modifications. Table 2 below provides a list of modified bases encompassed by the present invention.

TABLE 2 List of Base Modifications Abbreviation Chemical name m¹acp³Y 1-methyl-3-(3-amino-3-carboxypropyl) pseudouridine m¹A 1-methyladenosine m¹G 1-methylguanosine m¹I 1-methylinosine m¹Y 1-methylpseudouridine m¹Am 1,2′-O-dimethyladenosine m¹Gm 1,2′-O-dimethylguanosine m¹Im 1,2′-O-dimethylinosine m²A 2-methyladenosine ms²io⁶A 2-methylthio-N⁶-(cis-hydroxyisopentenyl) adenosine ms²hn⁶A 2-methylthio-N⁶-hydroxynorvalyl carbamoyladenosine ms²i⁶A 2-methylthio-N⁶-isopentenyladenosine ms²m⁶A 2-methylthio-N⁶-methyladenosine ms²t⁶A 2-methylthio-N⁶-threonyl carbamoyladenosine s²Um 2-thio-2′-O-methyluridine s²C 2-thiocytidine s²U 2-thiouridine Am 2′-O-methyladenosine Cm 2′-O-methylcytidine Gm 2′-O-methylguanosine Im 2′-O-methylinosine Ym 2′-O-methylpseudouridine Um 2′-O-methyluridine Ar(p) 2′-O-ribosyladenosine (phosphate) Gr(p) 2′-O-ribosylguanosine (phosphate) acp³U 3-(3-amino-3-carboxypropyl)uridine m³C 3-methylcytidine m³Y 3-methylpseudouridine m³U 3-methyluridine m³Um 3,2′-O-dimethyluridine imG-14 4-demethylwyosine s⁴U 4-thiouridine chm⁵U 5-(carboxyhydroxymethyl)uridine mchm⁵U 5-(carboxyhydroxymethyl)uridine methyl ester inm⁵s²U 5-(isopentenylaminomethyl)-2-thiouridine inm⁵Um 5-(isopentenylaminomethyl)-2′-O-methyluridine inm⁵U 5-(isopentenylaminomethyl)uridine nm⁵s²U 5-aminomethyl-2-thiouridine ncm⁵Um 5-carbamoylmethyl-2′-O-methyluridine ncm⁵U 5-carbamoylmethyluridine cmnm⁵Um 5-carboxymethylaminomethyl-2′-O-methyluridine cmnm⁵s²U 5-carboxymethylaminomethyl-2-thiouridine cmnm⁵U 5-carboxymethylaminomethyluridine cm⁵U 5-carboxymethyluridine f⁵Cm 5-formyl-2′-O-methylcytidine f⁵C 5-formylcytidine hm⁵C 5-hydroxymethylcytidine ho⁵U 5-hydroxyuridine mcm⁵s²U 5-methoxycarbonylmethyl-2-thiouridine mcm⁵Um 5-methoxycarbonylmethyl-2′-O-methyluridine mcm⁵U 5-methoxycarbonylmethyluridine mo⁵U 5-methoxyuridine m⁵s²U 5-methyl-2-thiouridine mnm⁵se²U 5-methylaminomethyl-2-selenouridine mnm⁵s²U 5-methylaminomethyl-2-thiouridine mnm⁵U 5-methylaminomethyluridine m⁵C 5-methylcytidine m⁵D 5-methyldihydrouridine m⁵U 5-methyluridine tm⁵s²U 5-taurinomethyl-2-thiouridine tm⁵U 5-taurinomethyluridine m⁵Cm 5,2′-O-dimethylcytidine m⁵Um 5,2′-O-dimethyluridine preQ₁ 7-aminomethyl-7-deazaguanosine preQ₀ 7-cyano-7-deazaguanosine m⁷G 7-methylguanosine G⁺ archaeosine D dihydrouridine oQ epoxyqueuosine galQ galactosyl-queuosine OHyW hydroxywybutosine I inosine imG2 isowyosine k²C lysidine manQ mannosyl-queuosine mimG methylwyosine m²G N²-methylguanosine m²Gm N²,2′-O-dimethylguanosine m^(2,7)G N²,7-dimethylguanosine m^(2,7)Gm N²,7,2′-O-trimethylguanosine m² ₂G N²,N²-dimethylguanosine m² ₂Gm N²,N²,2′-O-trimethylguanosine m^(2,2,7)G N²,N²,7-trimethylguanosine ac⁴Cm N⁴-acetyl-2′-O-methylcytidine ac⁴C N⁴-acetylcytidine m⁴C N⁴-methylcytidine m⁴Cm N⁴,2′-O-dimethylcytidine m⁴ ₂Cm N⁴,N⁴,2′-O-trimethylcytidine io⁶A N⁶-(cis-hydroxyisopentenyl)adenosine ac⁶A N⁶-acetyladenosine g⁶A N⁶-glycinylcarbamoyladenosine hn⁶A N⁶-hydroxynorvalylcarbamoyladenosine i⁶A N⁶-isopentenyladenosine m⁶t⁶A N⁶-methyl-N⁶-threonylcarbamoyladenosine m⁶A N⁶-methyladenosine t⁶A N⁶-threonylcarbamoyladenosine m⁶Am N⁶,2′-O-dimethyladenosine m⁶ ₂A N⁶,N⁶-dimethyladenosine m⁶ ₂Am N⁶,N⁶,2′-O-trimethyladenosine o₂yW peroxywybutosine Y pseudouridine Q queuosine OHyW undermodified hydroxywybutosine cmo⁵U uridine 5-oxyacetic acid mcmo⁵U uridine 5-oxyacetic acid methyl ester yW wybutosine imG wyosine

Antibodies specific to modified bases have previously been described. For example, antibodies to m⁶A have been described by Munns et al., “Characterization of Antibodies Specific for N⁶-Methyladenosine and for 7-Methylguanosine,” Biochemistry 16:2163-2168 (1977), which is now commercially available through Synaptic Systems (SySy; Germany) and Kong et al., “Functional Analysis of Putative Restriction-Modification System Genes in the Helicobacter pylori J99 Genome,” Nucleic Acids Res. 28:3216-3223 (2000), both of which are hereby incorporated by reference in their entirety. Antibodies specific to 5-methylcytidine (m⁵C) have been shown to react with m⁵C in mammalian DNA bound to nitrocellulose paper (Achwal et al., “Immunochemical Evidence for the Presence of 5mC, 6mC and 7mG in Human, Drosophila and Mealybug DNA,” FEBS Lett. 158:353-358 (1983); Achwal & Chandra, “A Sensitive Immunochemical method for Detecting 5mC in DNA Fragments,” FEBS Lett. 150:469-472 (1982), each of which is hereby incorporated by reference in its entirety). Immunofluorescence has also been used to determine chromosomal regions with a high frequency of m⁵C (Barbin et al., “New Sites of Methylcytosine-rich DNA Detected on Metaphase Chromosomes,” Hum. Genet. 94:684-692 (1994), which is hereby incorporated by reference in its entirety). Mouse monoclonal antibody against 5-methylcytidine has also been used previously to detect alterations in the urinary excretion of nucleosides by cancer patients (Tebib et al., “Relationship Between Urinary Excretion of Modified Nucleosides and Rheumatoid Arthritis Process,” Br. J. Rheumatol. 36:990-995 (1997), which is hereby incorporated by reference in its entirety) and to visualize the distribution of methylated sequences along mammalian chromosomes in normal and malignant cells (Hernandez-Blazquez et al., “Evaluation of Global DNA Hypomethylation in Human Colon Cancer Tissues by Immunohistochemistry and Image Analysis,” Gut 47:689-693 (2000); Mayer et al., “Demethylation of the Zygotic Paternal Genome,” Nature 403:501-502 (2000), each of which are hereby incorporated by reference in their entirety).

Antibodies specific to methylated bases are available commercially. For example, a mouse monoclonal antibody against m⁵C is available from Eurogentec S.A. (Belgium) and a rabbit polyclonal serum is available from Megabase Research Products (USA). Polyclonal rabbit antisera against other methylated bases (6-methyladenosine and 7-methylguanosine) are also available (Megabase Research Products, USA).

An antibody against inosine is described in Inouye et al., “Detection of Inosine-containing Transfer Ribonucleic Acid Species by Affinity Chromatography on Columns of Anti-Inosine Antibodies,” J. Biol. Chem. 248:8125-8129 (1973), which is hereby incorporated by reference in its entirety.

Several companies sell antibodies agains hm⁵C, including Active Motif (http://www.activemotif.com/catalog/details/39769.html), Millipore (http://www.millipore.com/catalogue/item/mabe176), and Sigma (http://www.sigmaaldrich.com/catalog/product/sigma/sab4800002?lang=en&region=US), which are hereby incorporated by reference in their entirety.

Antibodies useful in the methods of the present invention can also be developed according to methods known and practiced by persons of ordinary skill in the art. Such antibodies may be monoclonal antibodies, polyclonal antibodies, or functional fragments or variants thereof. The term “antibody” as used herein should be construed as covering any specific binding substance having a binding domain with the required specificity. Thus, this term covers antibody fragments, derivatives, functional equivalents, and homologues of antibodies, including any polypeptide comprising an immunoglobulin binding domain, whether natural or synthetic, monoclonal or polyclonal. Chimeric molecules comprising an immunoglobulin binding domain, or equivalent, fused to another polypeptide are also included, as described in more detail infra.

Monoclonal antibody production can be effected by techniques that are well known in the art. Basically, the process involves first obtaining immune cells (lymphocytes) from the spleen of a mammal (e.g., mouse) that has been previously immunized with the antigen of interest either in vivo or in vitro. The antibody-secreting lymphocytes are then fused with myeloma cells or transformed cells, which are capable of replicating indefinitely in cell culture. The resulting fused cells, or hybridomas, are immortal, immunoglobulin-secreting cell lines that can be cultured in vitro. Upon culturing the hybridomas, the resulting colonies can be screened for the production of desired monoclonal antibodies. Colonies producing such antibodies are cloned and grown either in vivo or in vitro to produce large quantities of antibody. A description of the theoretical basis and practical methodology of fusing such cells is set forth in Kohler and Milstein, “Continuous Cultures of Fused Cells Secreting Antibody of Predefined Specificity,” Nature 256:495 (1975), which is hereby incorporated by reference in its entirety.

Mammalian lymphocytes are immunized by in vivo immunization of the animal (e.g., a mouse, rat, rabbit, or human). Such immunizations are repeated as necessary at intervals of up to several weeks to obtain a sufficient titer of antibodies. Following the last antigen boost, the animals are sacrificed and spleen cells removed.

Fusion with mammalian myeloma cells or other fusion partners capable of replicating indefinitely in cell culture is effected by standard and well-known techniques, for example, by using polyethylene glycol (“PEG”) or other fusing agents (see Milstein and Kohler, “Derivation of Specific Antibody-producing Tissue Culture and Tumor Lines by Cell Fusion,” Eur. J. Immunol. 6:511 (1976), which is hereby incorporated by reference in its entirety). This immortal cell line, which is preferably murine, but may also be derived from cells of other mammalian species, including but not limited to rats and humans, is selected to be deficient in enzymes necessary for the utilization of certain nutrients, to be capable of rapid growth, and to have good fusion capability. Many such cell lines are known to those skilled in the art, and others are regularly described. Human hybridomas can be prepared using the EBV-hybridoma technique monoclonal antibodies (see Cole et al., in Monoclonal Antibodies and Cancer Therapy, Alan R. Liss, Inc., pp. 77-96 (1985), which is hereby incorporated by reference in its entirety). Human antibodies may be used and can be obtained by using human hybridomas (Cote et al., “Generation of Human Monoclonal Antibodies Reactive with Cellular Antigens,” Proc. Natl. Acad. Sci. USA 80:2026-2030 (1983), which is hereby incorporated by reference in its entirety) or by transforming human B cells with EBV virus in vitro (Cole et al., in Monoclonal Antibodies and Cancer Therapy, Alan R. Liss, Inc., pp. 77-96 (1985), which is hereby incorporated by reference in its entirety). In addition, monoclonal antibodies can be produced in germ-free animals (see PCT/US90/02545, which is hereby incorporated by reference in its entirety).

Procedures for raising polyclonal antibodies are also well known. Typically, such antibodies can be raised by administering the antigen subcutaneously to rabbits, mice, or rats which have first been bled to obtain pre-immune serum. The antigens can be injected as tolerated. Each injected material can contain adjuvants and the selected antigen (preferably in substantially pure or isolated form). Suitable adjuvants include, without limitation, Freund's complete or incomplete mineral gels such as aluminum hydroxide, surface active substances such as lysolecithin, pluronic polyols, polyanions, peptides, oil emulsions, dinitrophenol, and potentially useful human adjuvants such as bacille Calmette-Guerin and Carynebacterium parvum. The subject mammals are then bled one to two weeks after the first injection and periodically boosted with the same antigen (e.g., three times every six weeks). A sample of serum is then collected one to two weeks after each boost. Polyclonal antibodies can be recovered from the serum by affinity chromatography using the corresponding antigen to capture the antibody. This and other procedures for raising polyclonal antibodies are disclosed in Harlow & Lane, editors, Antibodies: A Laboratory Manual (1988), which is hereby incorporated by reference in its entirety.

In addition, techniques developed for the production of chimeric antibodies (Morrison et al., “Chimeric Human Antibody Molecules: Mouse Antigen-binding Domains with Human Constant Region Domains,” Proc. Natl. Acad. Sci. USA 81:6851-6855 (1984); Neuberger et al., “Recombinant Antibodies Possessing Novel Effector Functions,” Nature 312:604-608 (1984); Takeda et al., “Construction of Chimaeric Processed Immunoglobulin Genes Containing Mouse Variable and Human Constant Region Sequences,” Nature 314:452-454 (1985), each of which is hereby incorporated by reference in its entirety) by splicing the genes from a mouse antibody molecule of appropriate antigen specificity together with genes from a human antibody molecule of appropriate biological activity can be used. A chimeric antibody is a molecule in which different portions are derived from different animal species, such as those having a variable region derived from a murine mAb and a human immunoglobulin constant region (see e.g., U.S. Pat. No. 4,816,567 to Cabilly et al., and U.S. Pat. No. 4,816,397 to Boss et al., each of which is hereby incorporated by reference in its entirety).

Diabodies are multimers of polypeptides, each polypeptide comprising a first domain comprising a binding region of an immunoglobulin light chain and a second domain comprising a binding region of an immunoglobulin heavy chain, the two domains being linked (e.g., by a peptide linker) but unable to associate with each other to form an antigen binding site: antigen binding sites are formed by the association of the first domain of one polypeptide within the multimer with the second domain of another polypeptide within the multimer (see WO94/13804).

Alternatively, techniques described for the production of single chain antibodies (see e.g., U.S. Pat. No. 4,946,778 to Ladner et al.; Bird et al., “Single-chain Antigen-binding Proteins,” Science 242:423-426 (1988); Huston et al., “Protein Engineering of Antibody Binding Sites: Recovery of Specific Activity in an Anti-dogoxin Single-chain Fv Analogue Produced in Escherichia coli,” Proc. Natl. Acad. Sci. USA 85:5879-5883 (1988); Ward et al., “Binding Activities of a Repertoire of Single Immunoglobulin Variable Domains Secreted from Escherichia coli,” Nature 334:544-546 (1989), each of which is hereby incorporated by reference in its entirety) can be adapted to produce single chain antibodies against modified bases. Single chain antibodies are formed by linking the heavy and light chain fragments of the Fv region via an amino acid bridge, resulting in a single chain polypeptide.

In addition to utilizing whole antibodies, binding portions of such antibodies can be used. Such binding portions include Fab fragments, F(ab′)₂ fragments, and Fv fragments. These antibody fragments can be made by conventional procedures, such as proteolytic fragmentation procedures, as described in Goding, Monoclonal Antibodies: Principles and Practice, Academic Press (New York), pp. 98-118 (1983), which is hereby incorporated by reference in its entirety. Alternatively, the Fab fragments can be generated by treating the antibody molecule with papain and a reducing agent. Alternatively, Fab expression libraries may be constructed (see Huse et al., “Generation of a Large Combinatorial Library of the Immunoglobulin Repertoire in Phage Lambda,” Science 246:1275-1281 (1989), which is hereby incorporated by reference in its entirety) to allow rapid and easy identification of monoclonal Fab fragments with the desired specificity.

Antibodies may be isolated by standard techniques known in the art, such as immunoaffinity chromatography, centrifugation, precipitation, etc. The antibodies (or fragments or variants thereof) are preferably prepared in a substantially purified form (i.e., at least about 85% pure, more preferably 90% pure, even more preferably at least about 95% to 99% pure).

In one embodiment, the antibody used in the methods of the present invention is an anti-m⁶A antibody.

According to the present invention, conditions effective to bind an antibody to a modified base are known or can be determined by persons of ordinary skill in the art. For example, an appropriate ionic balance in the sample can assist the antibody in effectively binding to the modified base. The pH of a sample can be controlled by addition of suitable buffers such as sodium phosphate, which will maintain the pH at approximately 7.0. Salts, such as sodium chloride may also be added to the buffer and/or the sample. Moreover, maintenance of the sample at approximately 1° to 5° C., whilst contacting it with the antibody, may be preferred.

Obtaining a transcriptome for use in the methods of the present invention may be from a cell or tissue using methods well known to persons of ordinary skill in the art. Such methods may include, for example, polysome fractionation, poly(A) purification, exome capture, ribo-depletion, size fractionation, phenol-chloroform extraction, 70% ethanol elution, 100% ethanol elution, microRNA/small RNA isolation, or isolation of any RNA type. RNA transcripts of a transcriptome may also be obtained by coprecipitation/copurification with any protein, or with any complex, including ribosomal complexes. Thus, the methods of the present invention may be used to determine the base modification status of a transcriptome across an entire transcriptome group of RNA transcripts, or RNA transcripts of a particular RNA subtype, depending on which particular methods of obtaining the transcriptome are employed.

The methods of the present invention involve isolating, from the transcriptome, a pool of RNA transcripts to which the antibody binds. The pool of RNA transcripts may include full-length RNA transcripts or fragments of RNA derived from the full-length RNA transcripts. Thus, antibody-bound RNA transcripts can be separated from RNA transcripts to which no antibody is bound. Such isolating step can be carried out by various methods which are known by those of ordinary skill in the art.

According to one embodiment, isolating (or separating) is carried out by immunoprecipitation methods by attaching or binding an antibody to a solid phase or substrate (the terms are used interchangeably) and separating this solid phase from a sample liquid phase. Thus, addition of a solid substrate that binds specifically to an antibody facilitates the separation of an RNA transcript containing a modified base from an RNA transcript not having a modified base to which the antibody binds. Specific binding of the solid substrate to the antibody can be achieved by using a solid substrate that comprises a second antibody specific for the first antibody. For example, if the first antibody (i.e., the antibody specific to a modified base) is a mouse anti-m⁶A antibody, a goat anti-mouse antibody would be suitable.

A solid substrate in the form of beads is particularly useful as this gives a large surface area over which binding can occur. Magnetic beads such as Dynabeads (Dynal Biotech) or paramagnetic beads allow simple separation of modified and non-modified RNA transcripts as the beads (and, therefore, the nucleic acid bound to them) can be easily removed from a sample using a magnet. Other substrates, such as resin (e.g., agarose) or glass or other solid supports (e.g., ELISA plates) may also be used. Alternatively, the solid substrate could be separated from the non-bound nucleic acid using techniques such as centrifugation and/or filtration. A person of ordinary skill in the art can readily determine a suitable way to separate the solid substrate from non-bound (i.e. non-modified) RNA transcripts.

If desired, RNA can also be crosslinked to the antibody to facilitate RNA pulldown, which is common in the CLIP and PAR-CLIP type approaches. In some cases, the crosslinking is made more efficient if the RNA contains a nucleotide that has chemical modifications that enable it to crosslink to protein more efficiently.

In the methods of the present invention, RNA transcripts from the transcriptome may be fragmented before contacting the transcriptome with an antibody. Thus, the transcriptome that is contacted by the antibody may be a collection of RNA transcript fragments or full-length, and, therefore, the pool of RNA transcripts isolated from the transcriptome (as a result of antibody binding) would also constitute RNA transcript fragments.

Suitable RNA transcript fragments may include fragments having an average length of about 200 nucleotides, 150 nucleotides, 100 nucleotides, 50 nucleotides, 25 nucleotides, 10 nucleotides, or any combination thereof.

Fragmentation can be carried out by standard methods known in the art, e.g., sonication, chemical means such as hydrolysis, or enzyme digestion.

In carrying out the methods of the present invention, it may be desirable to sequence the RNA transcripts or RNA transcript fragments, both in the transcriptome and in the isolated pool. In one embodiment, sequencing of RNA transcripts is carried out by next-generation sequencing.

In addition, conventional nucleic acid analysis techniques may be applied to the RNA transcripts to perform additional analyses. For example, the presence of sequences of interest in the RNA transcripts may be determined using techniques such as PCR, slot blots, microarrays, etc., all of which are well known to those of ordinary skill in the art. According to one embodiment, a microchip system comprising a microarray of oligonucleotides or longer nucleotide sequences on a glass support is employed. Sample nucleic acid (e.g., fluorescently labelled) may be hybridized to the oligonucleotide array and sequence specific hybridization may be detected by scanning confocal microscopy and analyzed automatically (see Marshall & Hodgson, “DNA Chips: An Array of Possibilities,” Nature Biotechnology 16:27-31 (1998); see also Schulze et al., “Navigating Gene Expression Using Microarrays—A Technology Review,” Nature Cell Biology 3:E190-E195 (2001), each of which is hereby incorporated by reference in its entirety). A list of currently used techniques in microarray assembly and DNA detection can be found in the book DNA Microarrays: A Molecular Cloning Manual, eds. Bowtell and Sambrook, CSHL 2002.

Before sequencing and/or analyzing the isolated RNA transcripts, it may be desirable to detach the RNA molecule from the antibody (and any solid substrate used). Detaching methods for isolating RNA are known to persons of ordinary skill in the art. In carrying out detaching methods, care should be given to not damage the RNA transcript during the detaching process.

According to one embodiment, RNA transcripts are detached from antibodies by digesting the antibodies. This may be achieved by incubating the RNA transcript bound to the antibody with a proteinase such as Proteinase K. In addition, slightly altering the pH around the RNA transcript bound to the antibody may weaken the binding between the antibody and the RNA transcript, further facilitating detachment. This may be achieved by adding a suitable buffer (e.g., 50 mM Tris pH 8.0) to the RNA transcript and the antibody bound to it. EDTA (Ethylenediaminetetraacetic acid) and SDS (sodium dodecyl sulphate) may also be added to the buffer to assist in antibody detachment.

Once they have been detached from the antibodies and the solid substrate and sequenced, the RNA transcripts/fragments are analyzed further to determine the relative abundance of the isolated RNA transcripts present in the isolated pool. Measuring the abundance of an isolated RNA transcript fragment is carried out using a computational algorithm (FIG. 13). The parameters of the computational algorithm can be adjusted to enable more/less stringent detection of modified base sites in RNA transcripts, and also as to whether or not the user wants to require the presence of a peak across all replicates. See Saletore et al., “The Birth of the Epitranscriptome: Deciphering the Function of RNA Modifications,” Genome Biology 13:175 (2012), which is hereby incorporated by reference in its entirety. According to the methods of the present invention, isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome are identified. Each of the isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome.

The modified base status of a transcriptome is also characterized by detecting clusters of modified base reads that form a peak using the algorithm described in FIG. 13 (see also Meyer et al., “Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and Near Stop Codons,” Cell 149:1635-1646 (2012) and Dominissini et al, “Topology of the Human and Mouse m⁶A RNA Methylomes Revealed by m⁶A-seq,” Nature 485:201-206 (2012), each of which is hereby incorporated by reference in its entirety). Generally, this involves the following steps: (1) taking raw sequence data; (2) performing a gap alignment of reads to the genome (r-make); (3) applying the MeRIPPeR algorithm to find regions that are significantly different; (4) calling and ranking peaks for significance and degree of change; and (5) annotating and analyzing the peaks. In all likelihood, these RNA fragments will be higher in relative abundance in the isolated pool than in the non-isolated pool (i.e., transcriptome), but the specific appearance of a “peak” is used to characterize the modified base status of the transcriptome.

Application of the computational algorithm for measuring the abundance of an isolated RNA transcript fragment to characterize the modified base status of a transcriptome may be carried out with certain systems and devices. An exemplary environment 26 with a statistical computing device 10 that can be used to identify isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome to characterize the modified base status of the transcriptome is illustrated in FIG. 12. The environment 26 includes statistical computing device 10, communication network 22 and database server 24, although the environment can include other types and numbers of devices, components, elements and communication networks in other topologies and deployments. This technology/process provides a number of advantages including providing more effective methods, non-transitory computer readable medium, and devices for characterizing the modified base status of a transcriptome.

The statistical computing device 10 assists with sequencing and quantifying the abundance of RNA transcripts, although the statistical computing device 10 may perform other types and numbers of functions. The statistical computing device 10 includes at least one processor 12, memory 14, input and display devices 16, and interface device 18 which are coupled together by a bus 20 or other link, although the statistical computing device 10 may comprise other types and numbers of elements in other configurations.

Processor(s) 12 may execute one or more computer-executable instructions stored in the memory 14 for the methods illustrated and described with reference to the examples herein, although the processor(s) can execute other types and numbers of instructions and perform other types and numbers of operations. The processor(s) 12 may comprise one or more central processing units (“CPUs”) or general purpose processors with one or more processing cores, such as AMD® processor(s), although other types of processor(s) could be used (e.g., Intel®).

Memory 14 may comprise one or more tangible storage media, such as RAM, ROM, flash memory, CD-ROM, floppy disk, hard disk drive(s), solid state memory, DVD, or any other memory storage types or devices, including combinations thereof, which are known to those of ordinary skill in the art. Memory 14 may store one or more non-transitory computer-readable instructions of this technology as illustrated and described with reference to the examples herein that may be executed by the one or more processor(s) 12. The flow shown in FIG. 13 is representative of example steps or actions of this technology that may be embodied or expressed as one or more non-transitory computer or machine readable instructions stored in memory 14 that may be executed by the processor(s) 12.

Input and display devices 16 enable a user, such as an administrator, to interact with statistical computing device 10, such as to input and/or view data and/or to configure, program, and/or operate it by way of example only. Input devices may include a keyboard and/or a computer mouse and display devices may include a computer monitor, although other types and numbers of input devices and display devices could be used.

The interface device 18 in the statistical computing device 10 is used to operatively couple and communicate between the statistical computing device 10 and the database server 24 which are coupled together by a communication network. The communication network 22 can be a local area network (LAN) and/or a wide area network (WAN), although other types and numbers of communication networks or systems with other types and numbers of connections and configurations to other devices and elements can also be used. By way of example only, the local area networks (LAN) and the wide area network (WAN) can use TCP/IP over Ethernet and industry-standard protocols, including NFS, CIFS, SOAP, XML, LDAP, and SNMP, although other types and numbers of communication networks, can be used. In this example, the bus 20 is a hyper-transport bus, although other bus types and links may be used, such as PCI.

The database server 24 processes requests received from the statistical computing device 10 via communication network 22 according to the HTTP-based application, RFC protocol, the CIFS or NFS protocol, or other application protocols. A series of applications may run on the database server 24 that allow the transmission of data, such as sequence data, requested by the statistical computing device 10. The patient database server 24 may provide data or receive data in response to requests directed toward the respective applications on the database server 24 from the statistical computing device 10. It is to be understood that the database server 24 may be hardware or software or may represent a system with multiple servers 16, which may include internal or external networks. In this example the patient database server 24 may be any version of Microsoft® IIS servers or Apache® servers, although other types of servers may be used.

Furthermore, each of the systems of the examples may be conveniently implemented using one or more general purpose computer systems, microprocessors, digital signal processors, and micro-controllers, programmed according to the teachings of the examples, as described and illustrated herein, and as will be appreciated by those of ordinary skill in the art.

In addition, two or more computing systems or devices can be substituted for any one of the systems or devices in any example. Accordingly, principles and advantages of distributed processing, such as redundancy and replication also can be implemented, as desired, to increase the robustness and performance of the devices and systems of the examples. The examples may also be implemented on computer system(s) that extend across any suitable network using any suitable interface mechanisms and traffic technologies, including, by way of example only, teletraffic in any suitable form (e.g., voice and modem), wireless traffic media, wireless traffic networks, cellular traffic networks, G3 traffic networks, Public Switched Telephone Network (PSTNs), Packet Data Networks (PDNs), the Internet, intranets, and combinations thereof.

The examples may also be embodied as a non-transitory computer readable medium having instructions stored thereon for one or more aspects of the technology as described and illustrated by way of the examples herein, which when executed by a processor (or configurable hardware), cause the processor to carry out the steps necessary to implement the methods of the examples, as described and illustrated herein.

As used herein, “characterizing the modified base status of a transcriptome” means whether and/or to what extent the RNA transcripts of the transcriptome, as identified by the isolated RNA transcripts in the isolated pool, contain modified bases. Thus, the transcriptome is characterized by which bases of its RNA transcripts are modified, as well as the proportion of bases in the transcriptome that are modified.

The methods of the present invention provide the ability to perform high-resolution mapping of individual modified base sites in RNA. To this end, a variety of techniques related to the mapping of protein-RNA interactions could be employed, such as RIP-Seq or CLIP-Seq. Increased fragmentation of RNA may be performed prior to both (i) contacting the transcriptome with an antibody and (ii) isolating antibody bound transcript fragments, RNase digestion could be employed following antibody/RNA binding to decrease the size of immunoprecipitated RNA transcript fragments, and/or UV-crosslinking of antibody/RNA transcript fragment complexes and detection of polymerase stalls/mutations induced by antibody-RNA adducts could all be used to map individual modified base sites.

The methods of the present invention can also be utilized to determine the regulatory interaction of modified base sites with transcription factors and protein cofactors in vivo and within in vitro systems. For example, it may be desirable to determine the specific proteins and factors that influence the levels of modified bases on a particular transcript, and the method of the present invention can be utilized to target such proteins. In addition, the method of the present invention can be used to determine the effect of modified base levels and cellular machinery for the metabolism and use-rate of modified bases in a cell or in vitro systems.

In one embodiment, characterization of the transcriptome is based on the presence of substituted bases. In another embodiment, characterization of the transcriptome is based on the presence of a methylated base, such as m⁶A, or any other modified (whether methylated or not) base, as described herein. In yet another embodiment, characterization of the transcriptome is based on the presence of both substituted and methylated bases.

In one embodiment, the methods of the present invention are employed to establish how modified base residues are distributed in the transcriptome of various types of cells. Thus, transcriptomes that may be used in the methods of the present invention include those obtained from a single cell type, a single tissue type, a diseased cell or tissue, or a non-diseased cell or tissue. Thus, for example, the transcriptome may be from a cancer biopsy or a blood sample so that the modified base status of a transcriptome from such a cell type may be characterized.

In another embodiment, the modified base status of a transcriptome from a first cell type or tissue type is compared to the modified base status of a transcriptome from a second cell type or tissue type. By comparing the modified base status of one transcriptome obtained from cell or tissue type to another cell or tissue type, it may be possible to determine if a particular biological sample has base modifications that are compatible with a specific disease. Also, such a method could be useful in determining the causes and/or effects of modified bases of a transcriptome. For example, in one embodiment, the first cell type or tissue type is from a non-diseased cell or tissue and the second cell type or tissue type is from a diseased cell or tissue. According to this particular embodiment, the modified base status of a diseased cell or tissue can be compared to the modified base status of a non-diseased cell or tissue to decipher the effect the modified base status of the transcriptome has on a particular disease state.

In another embodiment, the method of the present invention is carried out to determine how a modified base status of a transcriptome from one cell or tissue type is different from one disease state to another, thereby establishing etiology of a disease or biomarkers for disease states.

For example, the fat mass and obesity-associated (FTO) gene is a major regulator of metabolism and energy utilization (Church et al., “A Mouse Model for the Metabolic Effects of the Human Fat Mass and Obesity Associated FTO Gene,” PLoS Genet 5:e1000599 (2009); Church et al., “Overexpression of Fto Leads to Increased Food Intake and Results in Obesity,” Nat. Genet 42:1086-1092 (2010); Fischer et al., “Inactivation of the Fto Gene Protects From Obesity,” Nature 458:894-898 (2009), each of which is hereby incorporated by reference in its entirety). In humans, FTO polymorphisms that increase FTO expression are associated with elevated body mass index and increased risk for obesity (Fawcett et al., “The Genetics of Obesity: FTO Leads the Way,” Trends Genet. 26:266-274 (2010), which is hereby incorporated by reference in its entirety). FTO is a member of the Fe(II)- and oxoglutarate-dependent AlkB oxygenase family, and was originally shown to catalyze the oxidative demethylation of methylated thymidine and uracil (Gerken et al., “The Obesity-Associated FTO Gene Encodes a 2-Oxoglutarate-Dependent Nucleic Acid Demethylase,” Science 318:1469-1472 (2007); Jia et al., “Oxidative Demethylation of 3-Methylthymine and 3-Methyluracil in Single-Stranded DNA and RNA by Mouse and Human FTO,” FEBS Lett. 582:3313-3319 (2008), each of which is hereby incorporated by reference in its entirety). However, FTO exhibits low activity toward these base modifications, and they are relatively infrequent with unclear physiological relevance (Klagsbrun, M., “An Evolutionary Study of the Methylation of Transfer and Ribosomal Ribonucleic Acid in Prokaryote and Eukaryote Organisms,” J. Biol. Chem. 248:2612-2620 (1973), which is hereby incorporated by reference in its entirety). Thus, the physiologically relevant targets of FTO was unclear until recent studies that showed that FTO can demethylate the naturally occurring adenosine modification N⁶-methyladenosine (Jia et al., “N6-Methyladenosine in Nuclear RNA Is a Major Substrate of the Obesity-Associated FTO,” Nat. Chem. Biol. 7(12):885-887 (2011) and Meyer et al., “Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and Near Stop Codons,” Cell 149:1635-1646 (2012), each of which is hereby incorporated by reference in its entirety). These studies link adenosine methylation to physiological roles in human biological processes.

In another example, Zheng et al., “ALKBH5 Is a Mammalian RNA Demthylase that Impacts RNA Metabolism and Mouse Fertility,” Mol. Cell 49:1-12 (2013), which is hereby incorporated by reference in its entirety, describes a demethylase that oxidatively reverses m⁶A modification, and this reversible modification was shown to have a fundamental and broad function in mammalian cells.

Transcriptomes may also be obtained from experimentally treated cells, such as cancer cells treated with a test compound. By comparing the modified base status of a transcriptome from a treated sample to a non-treated sample, researchers could determine the affect of a test compound on the modified base status in a transcriptome and/or at a specific site within a specific RNA transcript. Thus, this method of the present invention may be useful in drug discovery. Recently, Chen et al., “Development of Cell-Active N⁶-Methyladenosine RNA Demethylase FTO Inhibitor,” J. Am. Chem. Soc. 134:17963-17971 (2012), which is hereby incorporated by reference in its entirety, described small-molecule inhibitors of human FTO demethylase that exhibit good inhibitory activity on m⁶A demethylation inside cells.

In another embodiment, the transcriptome may be obtained from genetically modified cells, such as cells that are expressing either a control construct or a construct that expresses a target gene of interest or a knockdown construct. By comparing the modified base status of a transcriptome from such a sample, it may be possible to determine whether a gene can be used to normalize modified base levels, or if a gene has a role in regulating modified base levels.

In a further embodiment, the transcriptome is obtained from stem cells, such as embryonic stem cells, differentiated stem cells, or induced pluripotent stem (IPS) cells. By comparing the modified base profiles from transcriptomes from such cell types, it may be possible to determine whether changes in modified base levels accompany cellular differentiation which is important for cell fate decisions and embryonic development.

Another aspect of the present invention relates to a method of diagnosis or prognosis of a disease or disorder associated with a modified base in RNA in a subject. This method involves obtaining a transcriptome from a subject, where the transcriptome comprises one or more modified bases; characterizing the modified base status of the transcriptome; and comparing the modified base status of the transcriptome to a known modified base status of a comparable transcriptome from a healthy and/or diseased subject to provide a diagnosis or prognosis of a disease or disorder in the subject.

In one embodiment, obtaining a transcriptome from a subject involves obtaining a sample of RNA. In another embodiment, obtaining a transcriptome from a subject involves obtaining a complete transcriptome from a subject.

A further aspect of the present invention relates to a method of determining the effect of a treatment on modified base levels of a transcriptome from a subject. This method involves characterizing the modified base status of a transcriptome comprising one or more modified bases from a subject before a treatment is administered to the subject; characterizing the modified base status of the transcriptome from the subject after a treatment is administered to the subject; and comparing the modified base status of the transcriptome after said treatment to the modified base status of the transcriptome before said treatment to determine the effect of the treatment on modified base levels of the transcriptome in the subject.

In carrying out these methods of the present invention, characterizing the modified base status involves contacting the transcriptome with an antibody specific to the one or more modified bases under conditions effective to bind the antibody to the one or more modified bases; isolating, from the transcriptome, a pool of RNA transcripts to which the antibody binds; and identifying isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome, where each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome.

In one embodiment, characterizing the modified base status of the transcriptome may involve isolating and purifying cells or tissues from the subject using standard techniques, and then characterizing the modified base status of the transcriptome from that cell or tissue.

According to one embodiment, modified base status may be detected in just one transcript, or group of transcripts, rather than the entire transcriptome.

The present invention also relates to a kit for characterizing the modified base status of a transcriptome. The kit includes an antibody that binds a modified base in RNA; one or more buffer solutions for carrying out antibody binding to a modified base in RNA, isolation of antibody-bound RNA, and/or sequencing of RNA molecules; and a computer-readable medium having stored thereon computer-readable instructions for comparing the abundance of an RNA transcript fragment in an isolated pool of RNA transcripts from a transcriptome to the abundance of that RNA transcript in the transcriptome prior to isolation thereof to identify isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome, where each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome.

The antibody and buffer solution components of the kit may be packaged either in aqueous media or in lyophilized form. The container means of the kit will generally include at least one vial, test tube, flask, bottle, syringe or other container means, into which a component may be placed, and preferably, suitably aliquoted. The kit also will generally contain a second, third or other additional container into which the additional components may be separately placed. However, various combinations of components may be comprised in a vial. The kits of the present invention also will typically include a means for containing the antibodies, and any other reagent containers in close confinement for commercial sale.

When the components of the kit are provided in one and/or more liquid solutions, the liquid solution is an aqueous solution, with a sterile aqueous solution being one preferred solution. Other solutions that may be included in the kit are those solutions involved in carrying out antibody binding to a modified base in RNA, isolation of antibody-bound RNA, sequencing of RNA molecules, separation of antibodies from bound RNA, and any other solutions needed in carrying out the methods of the present invention described herein.

The kit can also include instructions for employing the kit components, the use of any other reagent not included in the kit, and use or programming of the computer-readable medium. Preferably, the kit includes instructions for using the antibody that binds a modified base in RNA, necessary buffer solutions for carrying out methods of the present invention described herein, and a computer-readable medium having stored thereon instructions for comparing the abundance of an RNA transcript fragment in an isolated pool of RNA transcripts from a transcriptome to the abundance of that RNA transcript in the transcriptome prior to isolation thereof to identify isolated RNA transcripts that are present in higher abundance in the isolated pool relative to the transcriptome, where each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome. Instructions may include variations that can be implemented.

These aspects of the present invention are further illustrated by the examples below.

EXAMPLES

The following examples are provided to illustrate embodiments of the present invention, but they are by no means intended to limit its scope.

Materials and Methods for Examples 1-13

m⁶A Immunoprecipitation/RNA-Seq (MeRIP-Seq)—

Total mouse brain RNA was subjected to RiboMinus treatment to reduce rRNA content as per the manufacturer's instructions (Invitrogen). RNA was then fragmented to 100 nt-sized fragments using Illumina Fragmentation Buffer according to the manufacturer's instructions, and subjected to two rounds of m⁶A immunoprecipitation. Sequencing libraries were prepared using the Illumina protocol for mRNA samples, and sequencing was performed on an Illumina GAIIx or an Illumina HiSeq2000 as indicated. Genomic alignment (mm9 from UCSC genome browser) was done using the Burrows-Wheeler Aligner (BWA) (Li et al., “Fast and Accurate Long-read Alignment with Burrows-Wheeler Transform,” Bioinformatics 26:589-595 (2010), which is hereby incorporated by reference in its entirety) at default settings. Only those reads which (1) uniquely mapped to the genome and (2) had a Phred quality score greater or equal to 20 were analyzed.

Accession Numbers—

MeRIP-Seq data have been deposited in the GEO database under accession number GSE29714.

RNA Isolation—

Adult C57BL/6 mice (age 6-16 weeks) were sacrificed by CO₂ inhalation and cervical dislocation in accordance with the Weill Cornell Medical College Institutional Animal Care and Use Committee (IACUC). RNA from various tissues was immediately isolated using TRIzol according to the manufacturer's instructions (Invitrogen). For isolation of embryonic rat brain RNA, timed-pregnant female dams were sacrificed by CO₂ inhalation and cervical dislocation, and embryos were immediately removed and their brains isolated for RNA collection. Postnatal and adult rats were sacrificed as described for adult mice. HEK293T cell RNA was isolated from 10 cm dishes of HEK293T cells using TRIzol as above. For isolation of poly(A) RNA, total mouse brain RNA was subjected to two rounds of purification using oligo(dT)-coupled magnetic beads according to the manufacturer's instructions (Invitrogen). The uncaptured RNA after two rounds was designated as the “unhybridized RNA.” Lastly, for MeRIP-Seq experiments, total RNA was isolated as above and subjected to RiboMinus treatment (Invitrogen) prior to immunoprecipitation. Notably, although these samples likely contain more mature, spliced mRNA than pre-mRNA, it was observed that the percentage of intronic reads in the input RNA samples is consistent with that of rRNA-depleted samples (51% of total intronic and exonic sequences were introns), indicating that intronic sequences are indeed present within the input RNA.

Antibodies—

Two independently-derived antibodies generated against m⁶A were used in these studies. One is a rabbit polyclonal antibody originally developed by a research group in Germany (Munns et al., “Characterization of Antibodies Specific for N6-Methyladenosine and for 7-Methylguanosine,” Biochemistry 16:2163-2168 (1977), which is hereby incorporated by reference) and now commercially available through Synaptic Systems (SySy; Germany). The other is a separate rabbit polyclonal antibody of independent origin which was developed by a research group at New England Biolabs (NEB) (Kong et al., “Functional Analysis of Putative Restriction-Modification System Genes in the Helicobacter pylori J99 Genome,” Nucleic Acids Res. 28:3216-3223 (2000), which is hereby incorporated by reference in its entirety).

Cell Culture—

Cortical neurons were isolated from embryonic day 18 (E18) or postnatal day 3 (P3) rats and cultured according to established methods (Cohen et al., “Neurotrophin-Mediated Dendrite-to-Nucleus Signaling Revealed by Microfluidic Compartmentalization of Dendrites,” Proc. Natl. Acad. Sci. U.S.A. 108: 11246-11251 (2011), which is hereby incorporated by reference in its entirety). Briefly, cells were plated in Culture Media (Neurobasal supplemented with 1% penicillin/streptomycin, 1% Glutamax, and 2% NS21) (Chen et al., “NS21: Re-Defined and Modified Supplement B27 for Neuronal Cultures,” J. Neurosci. Methods 171:239-247 (2008), which is hereby incorporated by reference in its entirety). After 3 DIV, half the media was replaced with Culture Media+20 μM 5′-fluoro-2′-deoxyuridine (FdU) to eliminate dividing glial cells and obtain neuron-enriched cultures, and on every third day thereafter, one-third of the media was replaced with fresh Culture Media. Neurons were grown for 8-10 DIV and RNA isolated as described above. Immortalized cell lines were cultured in appropriate media (American Type Culture Collection; ATCC) and RNA was isolated as described above.

FTO Overexpression—

Overexpression experiments were carried out by infecting HEK293T cells with FLAG-tagged human FTO lentivirus or no virus control. Cells were cultured for 48 hours, and total RNA was isolated using TRIzol as above and then subjected to m⁶A immunoblotting. Additionally, heterologous overexpression of Flag-tagged human METTL3 was carried out in HEK293T cells using Lipofectamine 2000 according to the manufacturer's instructions (Invitrogen). RNA was isolated after 24 or 48 hours as above; however, increases in m⁶A levels detected by m⁶A immunoblotting were inconsistent.

m⁶A Immunoblotting—

RNA samples were quantified using UV spectrophotometry, and equal amounts were mixed 1:1 with glyoxal loading dye (Ambion) and denatured for 20 minutes at 50° C. Samples were then run on a 1% agarose gel for 1 hour at 70 V and transferred to a nylon membrane for 2-3 hours using the NorthernMax-Gly kit according to the manufacturer's instructions (Ambion). RNA was UV crosslinked to the membrane, and membranes were blocked for 1 hour in 5% nonfat dry milk in 0.1% PBST (0.1% Tween-20® in 1×PBS, pH 7.4) (Blocking Buffer). Rabbit anti-m⁶A antibody (SySy or NEB) or was diluted 1:1000 in 0.1% PBST and incubated on the membranes for 1 hour (25° C.) to overnight (4° C.). Following extensive washing with 0.1% PBST, HRP-conjugated donkey anti-rabbit IgG (GE Healthcare) was diluted 1:2500 in Blocking Buffer and added to the membranes for 1 hour at 25° C. Membranes were washed again in 0.1% PBST and developed with enhanced chemiluminescence (ECL; GE Healthcare).

Dot Blot Assays—

Dot blots were performed essentially as described for m⁶A immunoblotting (above). Two 25 nt-long oligonucleotides (Midland) were designed to contain either m⁶A or A at a single internal position (5′ AGTCGTTCATCTAGTTGCGGTGTAC 3′ (SEQ ID NO:1)) and were spotted onto a nylon membrane (GE Healthcare). The membrane was then UV crosslinked, blocked, and exposed to rabbit anti-m⁶A antibody as described above. For competition assays, rabbit anti-m⁶A antibody was pre-mixed with competitor RNA or competitor NTP for 30 minutes at 25° C. Competitor NTPs used were: N⁶-methyladenosine triphosphate, adenosine triphosphate, N¹-methyladenosine triphosphate, and 2′-O-methyladenosine triphosphate (TriLink). For experiments using competitor RNA, a PCR product was amplified from rat brain cDNA using the following primers:

(SEQ ID NO: 2) 5′ TAATACGACTCACTATAGGGTGTCACCAACTGGGA 3′ Fwd (T7 promoter sequence is italicized) and (SEQ ID NO: 3) 5′ ACCCTCATAGATGGGCACAG 3′ Rev. RNA was in vitro transcribed from this PCR product using the AmpliScribe T7-Flash kit (Epicentre). NTPs were added individually to the in vitro transcription reaction and included GTP, CTP, UTP and either N⁶-methyladenosine triphosphate (N⁶-MeATP) or adenosine triphosphate (ATP). Following pre-incubation with competitor NTPs or RNA, the m⁶A antibody was incubated on the membranes for 1 hour at 25° C. Membranes were then washed in 0.1% PBST, followed by incubation in secondary antibody (HRP-conjugated donkey anti-rabbit IgG, diluted 1:2500 in block) for 1 hour at 25° C. Membranes were again washed in 0.1% PBST and developed with ECL.

m⁶A DNA Immunoblotting—

Cells from from dam+ (DH5alpha; Invitrogen) or dam− (K12 ER2925; New England Biolabs) E. coli strains were grown overnight at 37° C. in 5 ml cultures of Luria-Bertani (LB) broth. Genomic DNA was isolated from each cell line and randomly sheared using a Branson sonicator (5 rounds of 7 sec pulses at 20% amplitude with 1 sec intervals). DNA was then treated with RNase A for 30 minutes at 37° C. to remove any RNA and quantified with UV spectrophotometry. 1 μg of dam+ DNA and 1.5 μg of dam− DNA were loaded into a 1% agarose gel and electrophoresed for 40 minutes at 100 V. DNA was passively transferred to a nylon membrane (GE Healthcare) for ˜2 hours with 20×SSC. Membrane was then UV crosslinked and blotted with anti-m⁶A as described above.

Densitometry—

Densitometry analysis of the relative abundance of m⁶A in immunoblot experiments was performed using the ImageJ software (Abramoff et al., “Image Processing with ImageJ,” Biophotonics International 11:36-42 (2004), which is hereby incorporated by reference in its entirety). The levels of m⁶A across various samples were determined relative to the levels of ethidium bromide staining of the corresponding 28S rRNA band (images of 28S rRNA band intensity were acquired prior to transferring the RNA from the gel to the membrane during immunoblotting).

m⁶A Immunoprecipitation—

For immunoprecipitation of RNA for MeRIP-Seq experiments, 12 μl rabbit anti-m⁶A antibody (Synaptic Systems or New England Biolabs) was coupled to sheep anti-rabbit Dynabeads (Invitrogen) in 300 μl 1 M IP Buffer (1 M NaCl, 10 mM sodium phosphate, 0.05% Triton-X) for 2 hours at 4° C. Beads were then washed 3 times in 300 μl 140 mM IP Buffer (140 mM NaCl, 10 mM sodium phosphate, 0.05% Triton-X). Fragmented RNA was denatured 5 minutes at 75° C., cooled on ice 2-3 minutes, and bound to antibody-coupled beads in 300 μl of 140 mM IP Buffer (2 hours at 4° C.). Beads were treated with 300 μl Elution Buffer (5 mM Tris-HCL pH 7.5, 1 mM EDTA pH 8.0, 0.05% SDS, 4.2 μl Proteinase K (20 mg/ml)) for 1.5 hours at 50° C., and RNA was recovered with phenol:chloroform extraction followed by ethanol precipitation.

To demonstrate the ability of the m⁶A antibody to immunoprecipitate targets that contain m⁶A, in vitro immunoprecipitation experiments were performed. m⁶A-containing DNA or RNA of known sequence was mixed with unmethylated DNA to achieve a heterogeneous population of both methylated and unmethylated targets, analogous to the mixture of both methylated and unmethylated RNA fragments that are used in MeRIP-Seq. The source of unmethylated DNA was a PCR product generated from rat brain cDNA using the following primers targeting β-actin:

(SEQ ID NO: 4) 5′ TGTCACCAACTGGGACGATA 3′ Fwd  and (SEQ ID NO: 5) 5′ ACCCTCATAGATGGGCACAG 3′ Rev.

The source of methylated DNA was a 3 kb fragment of the pcDNA3.1 vector (Invitrogen) obtained by restriction enzyme digest with PvuII. The digested vector was grown in dam+ cells (DH5alpha, Invitrogen), and the presence of m⁶A was confirmed with dam-sensitive restriction enzyme digest. For experiments using methylated RNA as a source of m⁶A, total mouse brain RNA was used in the input sample. For all assays, unmethylated DNA and methylated DNA/RNA were mixed 1:1 and used as the input sample. Immunoprecipitation using the m⁶A antibody was performed as described for MeRIP-Seq (above), and m⁶A target enrichment was measured with real-time quantitative PCR (qPCR; see below). For experiments using RNA as the source of m⁶A, cDNA was generated from both input and immunoprecipitated samples using random hexamers and Superscript III reverse transcriptase according to the manufacturer's instructions (Invitrogen) and then subjected to real-time qPCR analysis. For control experiments, MeRIP was performed using rabbit IgG in place of the m⁶A antibody. All other experimental parameters were kept the same.

Real-Time Quantitative PCR—

Real-time qPCR reactions were performed using iQ SYBR Green Supermix (Bio-Rad) and an Eppendorf Mastercycler ep realplex thermocycler. The ratio of methylated DNA (pcDNA3.1 or U2snRNA cDNA) to unmethylated DNA (β-actin) was determined for each sample, and enrichment for m⁶A targets was calculated relative to the input sample. All samples were run in duplicate. Primers used to amplify each target are as follows:

β-actin: (SEQ ID NO: 6) 5′ TGTCACCAACTGGGACGATA 3′ Fwd  and (SEQ ID NO: 7) 5′ ACCCTCATAGATGGGCACAG 3′ Rev; pcDNA3.1: (SEQ ID NO: 8) 5′ TGTAGGCGGTGCTACAGAGTTCTT 3′ Fwd and (SEQ ID NO: 9) 5′ TTTCTGCGCGTAATCTGCTGCTTG 3′ Rev; U2 snRNA: (SEQ ID NO: 10) 5′ GGCCTTTTGGCTAAGATCAAGTGT 3′ Fwd and (SEQ ID NO: 11) 5′ GGACGGAGCAAGCTCCTATTCCAA 3′ Rev; Rps14: (SEQ ID NO: 12) 5′ ACCTGGAGCCCAGTCAGCCC 3′ Fwd and (SEQ ID NO: 13) 5′ CACAGACGGCGACCACGACG 3′ Rev; Rps21: (SEQ ID NO: 14) 5′ CTGCGGAGGCACGAGCTACT 3′ Fwd and (SEQ ID NO: 15) 5′ TTCCGCGGCACGTACAGGTC 3′ Rev; Ndel1: (SEQ ID NO: 16) 5′ TGTCACCAACTGGGACGATA 3′ Fwd and (SEQ ID NO: 17) 5′ ACCCTCATAGATGGGCACAG 3′ Rev; Ptpn4: (SEQ ID NO: 18) 5′ CCTCCCATCCCGGTCTCCACC 3′ Fwd and (SEQ ID NO: 19) 5′ GGCTGCCCATCTTCAGGGGT 3′ Rev; Grm1: (SEQ ID NO: 20) 5′ GCCTCAGTGTGACGGTGGCC 3′ Fwd and (SEQ ID NO: 21) 5′ AGCTTGCCGTCACCGACGTG 3′ Rev; Drd1a: (SEQ ID NO: 22) 5′ TGTGTGGTTTGGCTGGGCGA 3′ Fwd and (SEQ ID NO: 23) 5′ TGGAGATGGAGCCTCGGGGC 3′ Rev; Tlr3: (SEQ ID NO: 24) 5′ TGCTCAGGAGGGTGGCCCTT 3′ Fwd and (SEQ ID NO: 25) 5′ CGGGGTTTGCGCGTTTCCAG 3′ Rev.

3′ Rapid Amplification of cDNA Ends (RACE)—

RACE-ready cDNA was generated from equal amounts of total poly(A) RNA, poly(A) tail-depleted RNA, or poly(A) tail-only RNA using a 1:1 ratio of the GeneRacer oligo(dT) primer (Invitrogen) and random hexamers according to the manufacturer's instructions (Invitrogen). PCR was carried out using Phusion High-Fidelity PCR Master Mix (Finnzymes).

RNA Pull-Down—

In experiments designed to validate the presence of m⁶A in specific transcripts, RNAs were selectively pulled down using biotinylated (attached at the 3′ or 5′ end) DNA probes. These probes were 50 nt in length, and were complementary to target mRNAs. The probes (300 pg) were mixed with 10 μg total mouse brain RNA in 50 μl total volume of hybridization buffer (2×SSC, 40 U/ml RNaseOUT, 300 ng/ml salmon sperm DNA). Samples were denatured 3 minutes at 75° C. and hybridized 30 minutes at 37° C. with occasional mixing. Meanwhile, 100 μl of MyOne T1 streptavidin-coupled Dynabeads (Invitrogen) were equilibrated by washing twice in 100 μl Binding/Washing (B&W) Buffer (5 mM Tris-HCl, 0.5 mM EDTA, 1 M NaCl), once in 0.1 M NaOH, and once in 0.1 M NaCl. Beads were then resuspended in 100 μl B&W Buffer+1 μg yeast tRNA (Roche) and incubated at room temperature for approximately 15 minutes. Buffer was then replaced with 50 μl B&W Buffer, and beads were added to the probe/RNA hybridization mix and incubated 10 minutes at room temperature with gentle rotation. Following 3 washes in B&W Buffer, 1 wash each in 0.5×SDS, 1×SSC, and 0.2×SSC, beads were resuspended in 100 μl Elution Buffer (10 mM EDTA, pH 8.0, 95% formamide), and biotinylated probe:RNA complexes were eluted by heating for 2 minutes at 90° C. Eluate was then digested with RNase H for 20 minutes at 37° C. and ethanol precipitated.

Unique Properties of MeRIP-Seq Data—

Although MeRIP-Seq is conceptually similar to CLIP-Seq, there are important technical differences between the two methods which necessitate the use of unique strategies for analyzing MeRIP-Seq data. In CLIP-Seq, endogenous RNA binding protein (RBP)-RNA interactions are stabilized by UV crosslinking and then partially digested with ribonucleases, leaving a unique “footprint” of RNA that is protected from digestion by the presence of the bound protein. The protein of interest is then immunoprecipitated (along with any bound RNAs). These RNA-RBP complexes are then end-labeled, separated by SDS-PAGE, and transferred to nitrocellulose. The short RNA fragments, or “tags,” are then isolated in accordance with the predicted size of the RBP, allowing them to be separated from any non-RBP bound RNA. Finally, these short RNA tags are then subjected to high-throughput sequencing, and the location of RBP binding sites can be determined by identifying regions of the transcriptome that contain unique overlapping tags (Ule et al., “CLIP: A Method for Identifying Protein-RNA Interaction Sites in Living Cells,” Methods 37:376-386 (2005), which is hereby incorporated by reference in its entirety). MeRIP-Seq, however, seeks to identify the location of a unique methylated base throughout the transcriptome. Instead of immunoprecipitating full-length RNA directly and then digesting it to produce protected fragments (as in CLIP-Seq), MeRIP-Seq digests the RNA first, then immunoprecipitates m⁶A-containing RNA fragments with an antibody that recognizes m⁶A. These fragments are then subjected to high-throughput sequencing, and are analogous to the tags sequenced in CLIP-Seq studies. The method for RNA fragmentation in MeRIP-Seq follows the recommendations of the sequencing platform used (Illumina) and digests RNAs to a range of sizes approximately 100 nt long. Therefore, one or more m⁶A residues could lie within each 100 nt-long tag that is sequenced. In contrast, CLIP-Seq tags are defined by the small regions in RNA that are protected from digestion by a bound RBP and thus identify a narrow region of RNA in which a given RBP binds.

This difference in the way CLIP-Seq and MeRIP-Seq tags are generated is important, because it translates to the use of different methods for identifying areas of CLIP-Seq and MeRIP-Seq tag clustering throughout the transcriptome. Both techniques are susceptible to non-specific immunoprecipitation of RNA, which can occur when RNA is binds to beads or other surfaces used in immunoprecipitation protocols. These non-specifically bound RNAs are presumably degraded by the ribonuclease digestion step in CLIP-Seq and are further excluded following the SDS-PAGE and subsequent size selection steps. However, such a step does not exist in the MeRIP-Seq protocol, as the addition of ribonucleases would lead to the degradation of m⁶A-containing RNAs as well. Thus, MeRIP-Seq data requires the pool of RNA prior to m⁶A immunoprecipitation to be sequenced in parallel. This population of RNA provides a measure of the abundance of each individual transcript prior to immunoprecipitation; thus, a comparison of tags from m⁶A immunoprecipitation (the MeRIP sample) to those from RNA prior to immunoprecipitation (the non-IP sample) is necessary to distinguish the MeRIP tags that are significantly enriched due to recognition by the m⁶A antibody from those that are randomly immunoprecipitated. In addition, because MeRIP-Seq seeks to uncover information regarding the frequency of adenosine methylation (see below), it is necessary to take into account the abundance of individual transcripts prior to immunoprecipitation. Because m⁶A immunoprecipitation enriches for m⁶A-containing transcripts, and thus changes the abundance of individual RNA fragments in the MeRIP sample, analyzing non-IP sample tags is necessary to determine the abundance of individual transcripts.

Identification of m⁶A Peaks Genome-Wide—

In order to identify regions that contain m⁶A, a method for detecting MeRIP-Seq read peaks was developed according to their genomic annotations. To do this, the entire genome (mm9 and hg19 builds) was divided into 25 nt-wide discrete, non-overlapping windows and the number of reads in the MeRIP sample were compared to the number of reads in the non-IP (control) sample. Since the non-IP sample was generated from the same initial pool of RNA as the MeRIP sample, it serves as a measure of the abundance of individual transcripts that were in the MeRIP sample prior to immunoprecipitation. Thus, by comparing the number of reads in the MeRIP sample to those in the non-IP sample, any biases that might be caused by non-random fragmentation of the RNA or by variability in the abundance of individual transcripts were minimized.

The number of reads that mapped to a given window for the MeRIP sample and the non-IP sample were compared to the total number of reads in each, and Fisher's exact test was used to determine the probability of observing this under the null hypothesis for each window (the p-value). Fisher's exact test is non-parametric and makes no assumptions about the model underlying the data. To account for the multiple testing hypothesis with such a large number of independent statistical tests (i.e., the large number of individual windows), Benjamin-Hochberg was used to adjust the p-values to reduce the false discovery rate to 5%. A window was defined as significant when the adjusted p-value was ≦0.05 for each replicate. Then, Fisher's Method was used to combine p-values across replicates to calculate a final p-value for the window. This strategy made it possible to identify regions of MeRIP read clusters at high resolution (25 nt), while simultaneously filtering out those windows that reach significance by chance, because of artifacts in the data or because of sequencing errors. The present analysis revealed 93,074 significant 25 bp windows for all three replicates in the mouse tissue samples and 440,910 in the HEK293T tissue samples.

In order to identify m⁶A peaks throughout the genome, the sites at which these significant 25 nt-wide windows clustered together to form distinct peaks were next determined. The size of the individual RNA fragments from which all samples were prepared was approximately 100 nt. For each immunoprecipitated fragment, an m⁶A residue could technically exist at the 5′-most base of the fragment or at the 3′-most base of the fragment. Therefore, a single m⁶A residue could be part of immunoprecipitated RNA fragments that at their extremes contain bases 100 nt upstream or 100 nt downstream of the actual m⁶A site. Thus, when identifying m⁶A peaks, it was predicted that they would be approximately 200 nt wide at their base (FIGS. 4A-4C).

To determine the 200 nt-wide regions of significant m⁶A enrichment, adjacent significant windows were concatenated from the 25 nt analysis and concatenated windows that were smaller than 100 bp wide were filtered out. It was reasoned that because the RNA in each sample was sheared to approximately 100 nt-long fragments, MeRIP reads which clustered around m⁶A sites would be at the highest density within the central 100 nt-wide region of the peak. Thus, 100 nt was selected as the minimum size of the concatenated windows required for peak definition. In some cases, the length of the concatenated windows spanned >200 nt. In such cases, these regions were considered to have n m⁶A peaks, where n corresponds to the minimum number of m⁶A peaks that could result in a concatenated window of that length. Using this method for peak calling, 41,072 peaks total across all MeRIP-Seq mouse samples and 57,236 peaks total across HEK293T samples were identified. Of these peaks, 13,471 m⁶A were significant in all three mouse brain samples and 18,756 in all three HEK293T samples.

The stringent filtering criteria outlined above allowed the false discovery rate of the method of the present invention for identifying m⁶A sites to be minimized. The drawback to this approach, of course, is that the final list of high-confidence m⁶A sites identified is likely to be an underestimate of the true number of m⁶A sites throughout the genome. The 41,072 significant windows identified from the 25 nt analysis (above) likely contain many valid m⁶A sites, and provided is the list of coordinates for each of these significant windows.

Determining m⁶A Peaks Across the Transcriptome—

Determining m⁶A peaks across the genome (above) was necessary for identifying regions of m⁶A localization within intronic and intergenic regions. However, to determine the enrichment and clustering of m⁶A peaks within individual transcripts, m⁶A peaks across the transcriptome were also identified. To do this, each RefSeq exon were split into windows approximately 25 nt in size, using all known annotated transcript forms of each gene. The actual size of the window was computed by counting how many 25 nt windows would be needed to span each exon, and then distributing the base pairs evenly across those windows to avoid the creation of small windows at the end of each exon. TopHat (Trapnell et al., “TopHat: Discovering Splice Junctions With RNA-Seq,” Bioinformatics 25:1105-1111 (2009), which is hereby incorporated by reference in its entirety) was then used to align the original sequenced data to the genome, with RefSeq exon-exon splice junctions used for the known exon junctions parameter. The split size was also set to half of the sequence length of Sample 1 (16 nt), which had a smaller sequenced length than the other samples. The default split size (25 nt) was used for the other replicates.

The number of reads within each replicate that mapped to each window by using BEDTools' intersectBed was then determined. As with the genome-wide peak identification (above), the number of reads in the MeRIP sample was then compared to the number in the non-IP sample within each window and Fisher's exact test was used to compute p-values for the windows of each replicate. These p-values were then adjusted using Benjamini-Hochberg, and only those windows with p-values less than or equal to 0.05 in all samples were kept. Next, windows were concatenated, and those that did not join to span contiguous regions at least 100 bp in length across mature, spliced transcripts were filtered out. The remaining windows were split into peaks between 100-200 nt in size. This method identified 23,924 peaks across the transcriptome, which overlapped with 93% of the peaks in the high-confidence set (above).

The mergeBed program from BEDTools (Quinlan et al., “BEDTools: A Flexible Suite of Utilities for Comparing Genomic Features,” Bioinformatics 26:841-842 (2010), which is hereby incorporated by reference in its entirety) was then used to join these peaks into contiguous regions across the genome and then re-split them into individual 100-200 nt peaks. This method allowed the removal of redundant peaks which mapped to the same area of two or more transcript variants of a given gene, and it resulted in a total of 17,830 m⁶A peaks. To determine the overlap between these transcriptomics peaks and genomically-defined peaks in the “high-confidence” set (above), BEDTools' intersectBed was used. It was required that each peak must overlap at least 50% with another peak, setting the −f parameter to 0.5.

Estimation of False Discovery Rate for Identification of m⁶A Peaks—

Analyses of both the BWA-aligned sequences across the genome and the TopHat-aligned sequences across the transcriptome are susceptible to the multiple testing problem, which is caused by the large number of 25 nt windows being independently tested in each analysis. To account for this, the Benjamini-Hochberg method was used for error correction, which seeks to estimate the threshold at which a certain false discovery rate (FDR) is achieved. An FDR of 0.05 was chosen to be used, and the p-values were adjusted accordingly per sample. However, it is likely that the FDR for the high-confidence set of m⁶A peaks is actually lower than 0.05, because of the numerous filtration steps used to obtain this list of m⁶A peaks (above). First, a window was only considered significant if the adjusted p-value was less than or equal to 0.05 in all three replicates. Second, only significant windows were used that continuously spanned a region of 100 nt or more when joined. Thus, while the FDR is estimated to be around 0.05 per sample, it is likely less for the list of high-confidence peaks.

Annotation of m⁶A Peaks—

Peaks were annotated by applying BEDTools' intersectBed in a tiered fashion. First, RefSeq gene annotations were split into two subsets, those for protein coding genes and those for non-protein coding genes. The tiered system first mapped peaks to protein coding gene coding sequences, UTRs, exons, introns, and finally full genes, in that order. Those that mapped 90% were given priority, then 50%, and finally any by overlap. Then, the same was performed on non-protein coding gene annotations, in the same order. Duplicate mappings were removed such that a peak was mapped only once to any given RefSeq gene annotation. Because individual m⁶A peaks often mapped to multiple transcript variants of the same RNA, only one transcript variant and RefSeq accession number were used per gene when generating the list of 4,654 unique genes in the high-confidence set of m⁶A peaks. Additionally, only one transcript variant per gene was reported in Table 3 and Table 5, infra, which list the m⁶A peaks from genes with the greatest enrichment and the greatest number of m⁶A peaks, respectively.

Distribution of m⁶A Peaks and Samples—

The peak annotations from above were then compiled into the pie chart distributions for the mouse brain peaks (FIG. 5C) and the HEK293T peaks (FIG. 9D). The distribution for the control data sets was computed in a similar tiered fashion, but by comparing RefSeq annotations against the original control datasets. These percentages were then averaged across the replicate controls.

Analysis of m⁶A Peak Distribution Along an mRNA—

First, a subset of the RefSeq gene annotations was derived by taking only one transcript variant of each gene. Next, overlapping transcript variants were removed from the set to reduce any ambiguity in determining which transcript a peak is from. Peaks were then mapped into this single-transcript-variant non-overlapping RefSeq subset. If the peak fell within a gene exon, then its position within the mature transcript was calculated using the exon lengths. This was then converted to a position within the 5′ UTR, the coding sequence, or the 3′ UTR segments, and divided by the length of that region and multiplied by 100 to determine a percentile for where this peak fell. The percentile bin that the peak fell into was then incremented, and the bins were plotted as a percentage of the total number of peaks in the dataset.

For plotting m⁶A enrichment (FIG. 9C), BEDTools' intersectBed was used to first calculate the number of reads that mapped to each peak, for each sample and replicate, which was then compiled into a single file to store peak read counts. A similar procedure was then performed on all RefSeq gene exons, and then tabulated by gene to get read counts from all samples for mature transcripts. The peak enrichment was computed for each peak by dividing the number of MeRIP reads by the number of control reads that mapped to that peak, each normalized for the total number of reads that were mapped, for each replicate, and then averaged across the three replicates. Peaks that had control RPKMs of less than 1 or that were in genes that had control RPKMs of less than 1 were filtered out. These are still peaks, given their high number of reads in the MeRIP samples, but the lack of reads in the control skews the enrichment score. The peaks were then mapped to percentile bins for the 5′ UTR, CDS, and 3′ UTR regions as above. The purpose of this plot was to show the distribution of potential m⁶A sites and their cumulative enrichment, and so the sum of the enrichment scores at bin was used to accurately determine both the peak enrichment and the number of peaks in each bin.

Determining the Most Frequently Methylated m⁶A Peaks—

To determine the frequency of methylation at individual m⁶A peaks, the ratio of the number of reads in the MeRIP sample within the region defined by each m⁶A peak were calculated, normalized by the total number of reads mapped to the genome in that sample, to the RPKM of the gene that the peak resides in. This ratio was averaged across all three replicates, and then shown on a log₂ scale. This method made it possible to determine the relative frequency of methylation at a given m⁶A peak. However, m⁶A peaks may be due to the presence one or more m⁶A residues. Therefore, the determination of the m⁶A peaks with high degrees of methylation could reflect either the stoichiometry of a single m⁶A residue, or a cluster of highly adjacent m⁶A residues, each with potentially low or varying stoichiometry. In many cases, single MeRIP-Seq peaks contained only one m⁶A consensus motif, suggesting a single methylation site (FIG. 8B); however, until m⁶A sites can be determined transcriptome-wide with single nucleotide resolution, it is impossible to know for sure whether an m⁶A peak corresponds to a single or multiple m⁶A residues.

m⁶A Enrichment vs RPKM—

To compute the enrichment of human and mouse m⁶A peaks, the normalized number of MeRIP reads by the normalized number of control reads that mapped to each peak were divided and averaged across replicates. The RPKM was computed for the gene transcript that the peak fell into, averaged across control replicates.

Distribution of m⁶A Surrounding CDS Start and End Sites—

Using the filtered subset of RefSeq annotations that had only one transcript variant per gene and no overlapping regions, a .bed file of 10-bp windows 1 kb upstream and downstream of both the coding sequence start and end sites was created. Windows were generated with the transcriptome coordinates of the specific transcript, taking into consideration the length limitations of each transcript. For example, the coding sequence start windows would stop at the beginning of the 5′ UTR and the end of the coding sequence. These transcriptome windows were then translated into genomic coordinates and the peaks were translated as single by points at the center of each peak. BEDTools' intersectBed was used to count the number of peaks that fell into each window. The peak counts were then tabulated across all genes into two sets of 200 bins that represented 1 kbp upstream and downstream of both the CDSs and the CDSe. The bins were plotted with 100 bins on each side of the CDSs or CDSe, and the center point was computed as the average of the adjacent bins.

eRIP-Seq Gene Ontology—

Gene ontology (“GO”) analysis was performed using the DAVID bioinformatics database (Huang da et al., “Bioinformatics Enrichment Tools: Paths Toward the Comprehensive Functional Analysis of Large Gene Lists,” Nucleic Acids Res. 37:1-13 (2009); Huang da et al., “Systematic and Integrative Analysis of Large Gene Lists Using DAVID Bioinformatics Resources,” Nature Protocols 4:44-57 (2009), which are hereby incorporated by reference in their entirety). GO classification for cellular component, biological process, and molecular function were performed at default settings. To provide additional validation of the results, two separate analyses were performed using two different lists of genes as background for the mouse brain dataset: 1) the list of genes expressed in all MeRIP and non-IP samples combined and 2) a list of random genes taken from the mouse transcriptome.

Evolutionary Conservation and Motif Statistics—

Analysis of phylogenetic conservation was done by comparing PhyloP (Pollard et al., “Detection of Nonneutral Substitution Rates on Mammalian Phylogenies,” Genome Res. 20:110-121 (2010), which is hereby incorporated by reference in its entirety) scores of m⁶A peaks to those same peaks randomly shuffled within gene exons using BEDTools (Quinlan et al., “BEDTools: A Flexible Suite of Utilities for Comparing Genomic Features,” Bioinformatics 26:841-842 (2010), which is hereby incorporated by reference). PhyloP scores were computed for each using completeMOTIFs, which uses the phastCons scores from vertebrates. Significant differences in the distributions of the PhyloP scores were determined with the Kolmogorov-Smirnov (K-S) test in the R programming environment, using the stats library package. Motif analysis was done using FIRE (Elemento et al., “A Universal Framework for Regulatory Element Discovery Across All Genomes and Data Types,” Mol. Cell 28:337-350 (2007), which is hereby incorporated by reference in its entirety) with default RNA analysis parameters. For motif analysis, the sequence under the peaks located in RefSeq mRNAs were extracted and converted to the appropriate strand. MicroRNA analyses were performed using custom scripts and TargetScan miRNA predictions.

Analysis of m⁶A Localization to Splice Junctions—

The number of m⁶A peaks found at exon-exon junctions was determined by overlapping the set of 14,416 transcriptome-wide m⁶A peaks that fall within CDSs with exon-exon junctions compiled from known RefSeq exons. Four different sets of control “peaks” were also generated, which were used to establish a background level of overlap with exon-exon junctions. These control sets included 1) randomly generated peaks, 2) upstream adjacent region peaks, 3) downstream adjacent region peaks, and 4) mixed adjacent region peaks.

The set of random control peaks was generated with BEDTools' shuffleBed program to randomly shuffle regions of the same size as the m⁶A CDS peaks throughout coding sequence exons. Peaks were shuffled only to exons on the same chromosome, and the shuffleBed program was modified so that it would retain the transcript of the new exon to which it was mapped. By default, shuffleBed allows the new random peak to extend beyond the end of the exon; therefore, the code was further modified to allow it to map peaks up to 50 nt upstream of the start of an exon. To make this a fair comparison, the code was also modified to allow it to map peaks up to 50 nt upstream of the start of the exon. The peaks are between 100-200 nt in size, so 50 nt is enough to allow a peak to cross the 5′ junction but not so much that a peak would end up being mapped completely out of the exon. These shuffled peaks were then mapped to transcriptome coordinates (the coordinates of mature transcripts for individual transcript variants of a gene).

The adjacent region control sets of peaks were generated by taking the regions immediately 5′ (the upstream adjacent regions set), 3′ (the downstream adjacent regions set), or either 5′ or 3′ (the mixed adjacent regions set) to each m⁶A peak within a CDS. The size of each control peak matched that of the adjacent m⁶A peak which was located either up- or downstream. Additionally, if the region adjacent to an m⁶A peak contained another m⁶A peak, the next available adjacent region which did not contain an m⁶A peak was used. This step ensured that the control peaks were adjacent to, but not overlapping with, m⁶A peaks.

After generating all four sets of control peaks, the number of exon-exon junctions that overlapped with the peaks within each set was determined as above.

Poly(A) Site Analysis—

To determine the degree of overlap between poly(A) cleavage sites and m⁶A peaks within 3′ UTRs, a list of known poly(A) cleavage sites were used (Brockman et al., “PACdb: PolyA Cleavage Site and 3′-UTR Database,” Bioinformatics 21:3691-3693 (2005), which is hereby incorporated by reference in its entirety) and examined to determine whether 50 nt regions upstream of each cleavage site overlapped with the regions of m⁶A peaks in 3′ UTRs. The overlap between these poly(A) cleavage sites and randomly generated regions in the same 3′ UTRs were also examined. These random regions were generated by using the BEDTools shuffleBed program with RefSeq 3′ UTR as the inclusion regions and the chromosome flag set. Using shuffleBed, 100 different sets of random peaks were generated and the average of the number of intersections with polyA sites. BEDTools' intersectBed was used to determine the overlapping regions, with the −f flag set to 0.2 to require that at least one fifth of each window (20-40 nt) overlap with a polyA site. The peaks were shuffled a total of 100 times, and the average of the total number of overlaps with polyA sites was used for the random counts.

MicroRNA Expression Analysis—

A wildtype mouse brain miRNA expression profile, and mouse miRNA TargetScan target predictions were downloaded and mapped to RefSeq transcript 3′ UTRs. m⁶A peaks were mapped to the same RefSeq transcript 3′ UTRs. For each miRNA, the number of target transcripts were determined, i.e., the number of 3′ UTRs with at least 1 miRNA target. For each miRNA, the number of m⁶A peaks found in all 3′ UTRs targeted by the miRNA were determined. Then, the ratio between the number of m⁶A peaks and the number of target transcripts was calculated for each miRNA, so as to obtain an average number of m⁶A peaks per target 3′ UTR. Using this miRNA expression profile, the 25 least and 25 most expressed miRNAs in mouse brain were identified and average numbers of m⁶A peaks per target 3′ UTR for these two miRNA groups were compared using Wilcoxon tests and boxplots.

Comparison of Mouse Brain and HEK293T Datasets—

RefSeq gene annotations were used to compare the peaks found in the mouse brain tissue samples to those found in the HEK293T tissue cell line. The peaks were matched with RefSeq annotations, with priority given to coding sequences, then the UTRs, exons, introns, and lastly, full genes, using BEDTools' intersectBed. If a peak mapped to more than one CDS exon, for example, all matches were kept. Using Microsoft Excel, the official gene symbols present in each peak set were tabulated and compared to determine the gene symbols present in both datasets.

Analysis of m⁶A-Containing Transcripts—

The pattern of m⁶A peaks gives hints as to the stage at which the RNA can become methylated. A small percentage of m⁶A peaks were observed within intronic regions, which suggests that at least some mRNAs are methylated as immature pre-mRNAs within the nucleus. The methylation of pre-mRNAs is consistent with the nuclear localization of MT-A70, the adenosine methyltransferase (Bokar et al., “Purification and cDNA Cloning of The AdoMet-Binding Subunit of the Human mRNA (N6-adenosine)-Methyltransferase,” RNA 3:1233-1247 (1997), which is hereby incorporated by reference in its entirety). However, it was also found that 5% of m⁶A peaks throughout the transcriptome contain reads that span an exon-exon junction, indicating that mature, spliced transcripts contain m⁶A and that m⁶A might have roles in mRNA processing events that occur both within and outside of the nucleus.

A-to-I Editing Site Analysis—

It was sought to explore whether adenosine methylation serves to regulate the conversion of adenosine to inosine, which is mediated by ADAR enzymes. ADARs exhibit markedly reduced activity towards m⁶A compared to adenosine (Veliz et al., “Substrate Analogues for an RNA-Editing Adenosine Deaminase: Mechanistic Investigation and Inhibitor Design,” J. Am. Chem. Soc. 125:10867-10876 (2003), which is hereby incorporated by reference in its entirety), raising the possibility that adenosine methylation may act as a regulatory mechanism to control editing. m⁶A peaks were compared to a list of 2,545 total A-to-I editing sites identified in mouse and human (Bahn et al., “Accurate Identification of A-To-I RNA Editing in Human by Transcriptome Sequencing,” Genome Res. 1:142-150 (2011); Enstero et al., “A Computational Screen for Site Selective A-to-I Editing Detects Novel Sites in Neuron Specific Hu Proteins,” BMC Bioinformatics 11:6 (2010); Li et al., “Widespread RNA and DNA Sequence Differences in the Human Transcriptome,” Science 333:53-58 (2011); Maas et al., “Genome-Wide Evaluation and Discovery of Vertebrate A-to-I RNA Editing Sites,” Biochem. Biophys. Res. Commun. 412:407-412 (2011); Neeman et al., “RNA Editing Level in the Mouse is Determined by the Genomic Repeat Repertoire,” RNA 12: 1802-1809 (2006); Sakurai et al., “Inosine Cyanoethylation Identifies A-to-I RNA Editing Sites in the Human Transcriptome,” Nat. Chem. Biol. 6:733-740 (2010); Wahlstedt et al., “Large-Scale mRNA Sequencing Determines Global Regulation of RNA Editing During Brain Development,” Genome Res. 19:978-986 (2009), which are hereby incorporated by reference in their entirety). Human A-to-I editing sites were converted to mm9 genomic coordinates using LiftOver from the UCSC Genome Browser (Rhead et al., “The UCSC Genome Browser database: Update 2010,” Nucleic Acids Res. 38:D613-619 (2010), which is hereby incorporated by reference in its entirety). Genomic regions defined by m⁶A peaks in the high-confidence set were used to look for overlaps with A-to-I sites. The random regions used as a reference for background overlap were generated using BEDTools' shuffleBed program (with the −incl flag set to known RefSeq genes). Random regions were set to be the same size as the regions of m⁶A peaks and were investigated for overlaps with A-to-I sites. This process was repeated for 100 permutations of random regions, and the average number of overlaps with A-to-I sites was determined.

This analysis revealed that only 10 of these A-to-I sites overlapped with m⁶A peaks, compared to an average of 8.25 overlaps in the control regions, indicating that m⁶A peaks are not significantly overrepresented at A-to-I editing sites. (p=0.54; chi-square test).

Although the presence of m⁶A would technically inhibit A-to-I editing and, therefore, potentially explain this lack of association, both A-to-I editing and m⁶A peaks exhibit substoichiometric modification (Iwamoto et al., “Estimating RNA Editing Efficiency of Five Editing Sites in the Serotonin 2C Receptor by Pyrosequencing,” RNA 11:1596-1603 (2005); Narayan et al., “An In Vitro System for Accurate Methylation of Internal Adenosine Residues in Messenger RNA,” Science 242:1159-1162 (1988); Rana et al., “Analysis and In Vitro Localization of Internal Methylated Adenine Residues in Dihydrofolate Reductase mRNA,” Nucleic Acids Res. 18:4803-4808 (1990), each of which is hereby incorporated by reference in its entirety). Therefore, the absence of a correlation between these two modifications is unlikely to reflect a complete inhibition of A-to-I editing by adenosine methylation. Nevertheless, because a given adenosine could be methylated or deaminated at very low levels, the possibility that m⁶A inhibits A-to-I editing at some sites cannot be ruled out.

Example 1 Detection of m⁶A in Mammalian mRNA

Because m⁶A exhibits the same base pairing as unmodified adenosine, it is not readily detectable by standard sequencing or hybridization-based approaches. Additionally, m⁶A is not susceptible to chemical modifications which might otherwise facilitate its detection, such as bisulfite treatment which is used to detect m⁵C in DNA. The methods used thus far to detect m⁶A have involved treating cells with radiolabeled methionine, the precursor of the endogenous methylating agent S-adenosylmethionine, to impart radiolabeled methyl groups to adenosine (Csepany et al., “Sequence Specificity of mRNA N6-Adenosine Methyltransferase,” J. Biol. Chem. 265:20117-20122 (1990); Dubin et al., “The Methylation State of Poly A-Containing Messenger RNA from Cultured Hamster Cells,” Nucleic Acids Res. 2:1653-1668 (1975); Narayan et al., “An in vitro System for Accurate Methylation of Internal Adenosine Residues in Messenger RNA,” Science 242:1159-1162 (1988), each of which is hereby incorporated by reference in its entirety). Radiolabeled m⁶A residues are subsequently mapped with thin-layer chromatography or HPLC.

To simplify detection of m⁶A, an immunoblotting strategy was developed. For these experiments, a previously described anti-m⁶A antibody was used (Bringmann et al., “Antibodies Specific for N6-Methyladenosine React with Intact snRNPs U2 and U4/U6,” FEBS Lett. 213:309-315 (1987); Jia et al., “N6-Methyladenosine in Nuclear RNA Is a Major Substrate of the Obesity-Associated FTO,” Nat. Chem. Biol. 7(12):885-887 (2011); Munns et al., “Characterization of Antibodies Specific for N6-Methyladenosine and for 7-Methylguanosine,” Biochemistry 16:2163-2168 (1977), each of which is hereby incorporated by reference in its entirety). To ensure the specificity of this antibody for m⁶A, dot blots were performed using modified oligonucleotides immobilized to a membrane. The m⁶A antibody selectively bound to oligonucleotides containing a single m⁶A residue, and exhibited negligible binding to oligonucleotides containing unmodified adenosine (FIG. 1A). The binding was competed by incubating the antibody with increasing concentrations of an m⁶A-rich competitor RNA (FIG. 1B). However, RNA containing unmodified adenosine did not compete for binding. Furthermore, binding was competed by N⁶-methyladenosine triphosphate, but not by ATP or other modified adenosine triphosphates including N¹-methyladenosine and 2′-O-methyladenosine (FIG. 1C). Finally, to examine the specificity of the antibody in the context of other nucleotide sequences, the inventors took advantage of the fact that the enzyme encoded by the DNA adenine methylase (dam) gene in E. coli methylates the N⁶ position of adenosine in DNA. Upon subjecting digested DNA isolated from dam+ and dam− E. coli to immunoblotting using the m⁶A antibody, robust signals were found only in the DNA samples from the dam+ strain (FIG. 1D). Collectively, these data demonstrate the high sensitivity and selectivity of this antibody for m⁶A, as well as its ability to detect m⁶A within cellular nucleotide pools.

To explore the abundance of m⁶A within various RNA populations, RNA was isolated from several mouse tissues and subjected to immunoblot analysis using the m⁶A antibody (FIG. 2A). It was found that m⁶A was present in all RNA samples tested, indicating that this modified nucleotide is widely distributed in many tissues, with particularly high enrichment in liver, kidney, and brain (FIG. 2B). In addition, large differences were observed in the m⁶A content of various immortalized cell lines, including several cancer cell lines, which further indicates that large differences in m⁶A levels exist in different cell populations (FIG. 10A).

m⁶A immunoreactivity was detected in bands throughout the molecular weight range of the blot (˜0.2 kb to ˜10 kb), consistent with incorporation of m⁶A in mRNA. Indeed, fractionation of whole cellular RNA into polyadenylated and nonpolyadenylated RNAs indicates that m⁶A immunoreactivity is enriched in the polyadenylated RNA pool, which indicates that m⁶A in cellular RNA is localized to mature mRNA (FIG. 2C). To determine whether m⁶A is present in poly(A) tails, the poly(A) tail was selectively removed from cellular mRNA using oligo(dT) hybridization and RNase H treatment. Transcripts depleted of the poly(A) tail did not exhibit an appreciable reduction in m⁶A levels (FIG. 2D). In addition, immunoblotting poly(A) tails alone showed minimal m⁶A immunoreactivity. Together, these data demonstrate that m⁶A is primarily an internal modification which is largely absent from the poly(A) tail.

Example 2 Dynamic Regulation of m⁶A

The observation that m⁶A is highly enriched within the brain prompted the inventors to investigate the temporal dynamics of m⁶A levels during different stages of neural development. Immunoblotting RNA samples with the m⁶A antibody indicates that m⁶A is present in mRNA at low levels throughout embryogenesis but increases dramatically by adulthood (FIG. 3A). A similar increase in m⁶A levels was also observed in RNA isolated from embryonic and postnatal rat brain cultured neurons (FIG. 10B), which indicates that upregulation of m⁶A levels accompanies neuronal maturation.

Next, it was asked whether adenosine methylation is a dynamically regulated post-transcriptional modification and whether its levels can be regulated by specific demethylating enzymes. In the search for potential demethylating enzymes that act to remove the methyl group from m⁶A, members of the family of Fe(II)- and 2-oxoglutarate-dependent oxygenases were the focus, several of which have previously been shown to demethylate both DNA and RNA (Falnes et al., “Repair of Methyl Lesions in DNA and RNA by Oxidative Demethylation,” Neuroscience 145:1222-1232 (2007); Gerken et al., “The Obesity-Associated FTO Gene Encodes a 2-Oxoglutarate-Dependent Nucleic Acid Demethylase,” Science 318:1469-1472 (2007), each of which is hereby incorporated by reference in its entirety). Consistent with the findings of Jia et al., “N6-Methyladenosine in Nuclear RNA Is a Major Substrate of the Obesity-Associated FTO,” Nat. Chem. Biol. 7(12):885-887 (2011), which is hereby incorporated by reference in its entirety, it was observed that FTO decreased m⁶A levels when overexpressed in mammalian cells (FIG. 3B). Furthermore, it was found that overexpression of FTO resulted in a broad size range of RNAs that exhibit reduced m⁶A immunoreactivity (FIG. 3B).

Example 3 MeRIP-Seq Identifies m⁶A-Containing RNAs Throughout the Transcriptome

To obtain insight into potential roles for m⁶A, the characterization of its distribution throughout the transcriptome was sought. To do this, it was first determined whether the m⁶A antibody could be used to enrich m⁶A-containing RNAs. In vitro immunoprecipitation experiments showed that a single round of MeRIP produces ˜70-fold enrichment, and two rounds produce over 130-fold enrichment for m⁶A-containing targets. To identify m⁶A sites throughout the transcriptome, a method that combines m⁶A-specific methylated RNA immunoprecipitation (MeRIP) with next-generation sequencing (RNA-Seq) was developed. The procedure for MeRIP-Seq (outlined in FIG. 4A) involves randomly fragmenting the RNA to approximately 100 nt-sized fragments prior to immunoprecipitation. Because an m⁶A site could lie anywhere along the length of a given immunoprecipitated 100 nt fragment, sequencing reads are expected to map to a region which contains the m⁶A site near its center. At its extremes, this region would be predicted to be roughly 200 nt wide (100 nt up- and downstream from the m⁶A site) (FIG. 4B, 4C).

Next, MeRIP-Seq was used to identify m⁶A sites in total mouse brain RNA. Reads from the MeRIP sample frequently mapped to mRNAs and clustered as distinct peaks. As predicted, these peaks frequently converged to approximately 100 nt-wide regions near their midpoint (FIG. 4C). Furthermore, enrichment of reads in these regions was not observed in the non-IP control sample, which was composed of the input RNA prior to m⁶A immunoprecipitation, demonstrating the specificity of these peaks.

To determine the location of these peaks throughout the transcriptome, and thus characterize the regions of m⁶A localization, an algorithm for identifying m⁶A peaks was developed (see Example 1, supra). Additionally, replicate MeRIP-Seq experiments were performed utilizing (1) a different sequencing platform (Illumina's GAIIx vs HiSeq2000), (2) independently prepared RNA samples from different animals, and (3) an unrelated m⁶A antibody (Kong et al., “Functional Analysis of Putative Restriction-Modification System Genes in the Helicobacter pylori J99 Genome,” Nucleic Acids Res. 28:3216-3223 (2000), which is hereby incorporated by reference in its entirety), which exhibited similarly high specificity for m⁶A. The algorithm was employed to identify m⁶A peaks that met a minimum p-value (p≦0.05, Benjamini and Hochberg corrected) within each individual sample. From the three samples, a total of 41,072 distinct peaks in the RNAs of 8,843 genes were identified, which are named the “filtered” set of m⁶A peaks.

Of these peaks, 80% were detected in at least two different replicates. The high concordance between these samples indicates that MeRIP-Seq is highly reproducible across different sequencing platforms and using different m⁶A antibodies. For subsequent bioinformatic analyses, the list of 13,471 m⁶A peaks was used in RNAs from 4,654 genes which were detected in all three replicates. This list demonstrated the presence of m⁶A in a substantial fraction of the transcriptome and indicated that m⁶A is a common feature of mammalian mRNAs.

Example 4 m⁶A is Detected in Non-Coding RNAs

The majority of the high-confidence m⁶A peaks (94.5%) were found within mRNAs. However, it was also observed that 236 (1.1%) of the peaks mapped to non-coding RNAs (ncRNAs) that were annotated in the RefSeq database (Table 4). In addition, 588 m⁶A peaks did not map to a known RefSeq mRNA or ncRNA. To determine whether these unannotated peaks localize to ncRNAs predicted in other databases, they were aligned to genomic regions of a set of 32,211 ncRNAs from the RIKEN functional annotation of mouse (FANTOM3) dataset that were obtained from the mammalian noncoding RNA database (RNAdb; Pang et al., “RNAdb—A Comprehensive Mammalian Noncoding RNA Database,” Nucleic Acids Res. 33:D125-130 (2005), which is hereby incorporated by reference in its entirety). It was found that 216 of these peaks mapped to a FANTOM3 ncRNA (Table 4). All of these ncRNAs were greater than 200 nt in length, indicating that long ncRNAs are substrates for adenosine methylation. Additionally, when a set of conserved human lincRNAs were interrogated (Cabili et al., “Integrative Annotation of Human Large Intergenic Noncoding RNAs Reveals Global Properties and Specific Subclasses,” Genes Dev. 25:1915-1927 (2011), which is hereby incorporated by reference in its entirety) for overlaps with m⁶A peaks, nine additional peaks that overlapped with these lincRNAs were found (Table 4). Collectively, these data identify several classes of ncRNAs as targets of adenosine methylation.

Example 5 Biochemical Validation of m⁶A-Containing Transcripts

It was next sought to validate the presence of m⁶A in mRNAs identified with MeRIP-Seq. To do this, RNA pull-down assays were used to isolate individual mRNAs from total mouse brain RNA by hybridization to target-specific probes. Isolated mRNAs were then subjected to immunoblot analysis using the m⁶A antibody to detect the presence of m⁶A. Using this method, the presence of m⁶A within low density lipoprotein receptor (Ldlr) (FIGS. 5A, 5B), metabotropic glutamate receptor 1 (Grm1), and dopamine receptor D1A (Drd1a) were validated (FIGS. 7A-7E). These mRNAs were chosen to demonstrate the present invention's ability to validate m⁶A presence in transcripts with multiple methylation sites (Grm1 and Drd1a) as well as those with single m⁶A peaks (Ldlr). To further demonstrate that MeRIP-Seq selectively enriches for these endogenous methylated targets, qRT-PCR was performed on the unbound fractions after RNA precipitation with the m⁶A antibody. As expected, substantial immunodepletion of Grm1, Drd1a, and other methylated targets in the unbound fraction was observed. In contrast, transcripts which lack m⁶A peaks, such as Rps21 and Ndel1, were detectable at high levels in the unbound fraction (FIG. 7E).

Example 6 m⁶A-Containing mRNAs are Involved in Important Biological Pathways

To predict potential signaling pathways and cellular processes that involve m⁶A, the DAVID bioinformatics database was used to identify the gene ontology (GO) terms that are enriched for m⁶A-containing transcripts. It was found that genes encoding m⁶A-containing RNAs are involved in a variety of cellular functions, including transcriptional regulation, RNA metabolism, and intracellular signaling cascades. In addition, it was observed that m⁶A peaks mapped to many genes linked to neurodevelopmental and neurological disorders, such as Bdnf, Dscam, Lis1, and Ube3a, as well as the neurexins and several neuroligins. Collectively, these data demonstrated that m⁶A-containing RNAs are involved in a variety of biological pathways relevant to cellular signaling and disease.

Gene ontology (GO) analysis of genes encoding m⁶A-containing RNAs was performed for biological process, cellular component, and molecular function. The GO term, the number of genes that fall within each category, the percentage of total genes encoding m⁶A-containing mRNAs that fall within each category, the p-values obtained from GO enrichment analysis, the RefSeq IDs of the m⁶A-containing RNAs of each term, the Bonferroni corrected-p-values, and the false discovery rate were all determined.

Since m⁶A is a physiological target of FTO, it was sought to determine whether mRNAs whose levels have previously been shown to be influenced by FTO activity contain m⁶A. A list of 77 mRNAs whose levels are either up- or downregulated in the liver, skeletal muscle, or white adipose tissue of mice homozygous for a nonsynonymous FTO point mutation were examined (Church et al., “A Mouse Model for the Metabolic Effects of the Human Fat Mass and Obesity Associated FTO Gene,” PLoS Genet. 5:e1000599 (2009), which is hereby incorporated by reference in its entirety). mRNAs from seven genes which were significantly upregulated in FTO mutants (Acaca, Atf6, Bip, Gcdh, Irs1, Perk, and Xbp1) also contain m⁶A peaks. Intriguingly, some of these genes are involved in important metabolic pathways, raising the possibility that demethylation of the transcripts of these genes may contribute to the mechanism by which FTO regulates metabolism and energy homeostasis.

Example 7 Diverse Patterns of m⁶A Localization within Transcripts

Next, the pattern of adenosine methylation in mRNAs was characterized. mRNAs from many genes (46.0%) exhibit a single m⁶A peak, consistent with a single m⁶A site or a cluster of adjacent m⁶A residues. However, 37.3% contain two m⁶A peaks, 11.2% contain three peaks, and 5.5% contain four or more peaks. Several genes contain ten or more m⁶A peaks, suggesting the existence of multiple m⁶A residues along their length. Indeed, of the twenty genes that exhibited the largest number of m⁶A peaks, all had 15 or more m⁶A peaks along their length (Table 3, infra). Table 2 lists the top 20 mouse brain mRNAs identified by MeRIP-Seq as having the greatest number of m⁶A sites along their length.

TABLE 3 mRNAs Containing the Greatest Number of m⁶A Peaks. # RefSeq m6A Chr Start End Accession Name Peaks chr18 34380637 34481844 NM_007462 Apc 24 chr2 121115337 121136568 NM_032393 Mtap1a 23 chr9 15714636 16182675 NM_001080814 Fat3 21 chr11 55064111 55125759 NM_001029988 Fat2 19 chr13 55311142 55419686 NM_008739 Nsd1 19 chr15 72636029 72640754 NR_002864 Peg13 18 chr5 14514917 14863459 NM_011995 Pclo 17 chr5 42178777 42235554 NM_001081422 Bod11 17 chr6 22825501 23002916 NM_001081306 Ptprz1 17 chr14 93412919 94287951 NM_001081377 Pcdh9 17 chr10 79764564 79781001 NM_011789 Apc2 17 chr19 9063773 9093685 NM_009643 Ahnak 17 chr7 17079821 17200342 NM_172739 Grlf1 16 chr1 30859186 30920101 NM_001081080 Phf3 16 chr8 124122602 124175833 NM_080855 Zcchc14 16 chr11 117515602 117624753 NM_198022 Tnrc6c 16 chr1 20880702 20928837 NM_028829 Paqr8 16 chr4 34750358 34830197 NM_013889 Zfp292 16 chr16 91648068 91663563 NM_019973 Son 16 chr5 107926763 107941575 NM_001007574 A830010M20Rik 16

Additionally, of the genes that contain more than one m⁶A peak, 90.1% contain two or more contiguous m⁶A peaks, indicating that m⁶A sites are frequently clustered in adjacent regions along the transcript. Indeed, 32.8% of m⁶A peaks are part of clusters that contain three or more adjacent m⁶A peaks, indicating that m⁶A clustering is a common feature in methylated transcripts (FIG. 8A). 68 genes that have long (≧1 kb) stretches of contiguous m⁶A peaks were also identified (Table 4, infra), which likely indicates the presence of multiple m⁶A residues throughout these regions.

TABLE 4 Transcripts with Multiple Adjacent m⁶A Peaks. RefSeq Length Chr Start End Accession Name Spanned chr13 98016375 98016739 NM_007930 Enc1 1014 chr9 58337925 58338199 NM_176921 6030419C18Rik 1024 chr1 193732829 193733854 NM_009579 Slc30a1 1025 chr2 160774888 160775913 NM_173368 Chd6 1025 chr3 32418578 32419603 NM_144519 Zfp639 1025 chr7 95279165 95280190 NM_001081414 Grm5 1025 chr9 111294275 111295300 NM_001164659 Trank1 1025 chrX 61523268 61524293 NM_178740 Slitrk4 1025 chr16 96223704 96224729 NM_001103179 Brwd1 1025 chr2 79294197 79295205 NM_010894 Neurod1 1030 chr3 16104675 16105424 NM_172677 Ythdf3 1049 chr3 82196199 82197249 NM_001081230 Mtap9 1050 chr3 107990483 107991533 NM_146137 Amigo1 1050 chr9 16179392 16180442 NM_001080814 Fat3 1050 chr9 101003350 101004400 NM_001100451 Msl2 1050 chr1 49074547 49075597 NM_001110148 Mgat1 1050 chr18 39645651 39646701 NM_008173 Nr3c1 1050 chr1 58959643 58960718 NM_172406 Trak2 1075 chr7 51716878 51717953 NM_198250 Lrrc4b 1075 chr8 124124353 124125427 NM_080855 Zcchc14 1075 chr9 20241361 20242436 NM_011753 Zfp26 1075 chr11 22735963 22737038 NM_016888 B3gnt2 1075 chr12 93046957 93048032 NM_001039089 Sel11 1075 chr9 110148798 110149894 NM_013884 Cspg5 1096 chr1 136644451 136645551 NM_009307 Syt2 1100 chr8 75338622 75339722 NM_010687 Large 1100 chr11 60883429 60884529 NM_010603 Kcnj12 1100 chr19 23239297 23240397 NM_010638 Klf9 1100 chr2 28084698 28085823 NM_001038613 Olfm1 1125 chr2 28084698 28085823 NM_019498 Olfm1 1125 chr13 60862120 60863245 NM_029653 Dapk1 1125 chr11 79315458 79316529 NM_019409 Omg 1140 chr2 125565321 125566471 NM_177608 Secisbp21 1150 chr17 5342062 5343212 NM_001085355 Arid1b 1150 chr8 34496846 34498001 NM_152821 Purg 1155 chr2 168007289 168008464 NM_009628 Adnp 1175 chr6 56728743 56729918 NM_145958 Kbtbd2 1175 chr8 63149995 63151170 NM_027756 Mfap31 1175 chrX 163911865 163913040 NM_001033330 Frmpd4 1175 chr11 20625687 20626862 NM_181411 Aftph 1175 chr3 27140122 27141322 NM_178772 Nceh1 1200 chr10 34002445 34003645 NM_009433 Tspyl1 1200 chr12 112948203 112949403 NM_027404 Bag5 1200 chr17 32910441 32911641 NM_172458 Zfp871 1200 chr4 68422805 68424030 NM_019967 Dbc1 1225 chr7 71035186 71036436 NM_021366 Klf13 1250 chr4 49598302 49599577 NM_025944 2810432L12Rik 1275 chr7 13395301 13396576 NM_026046 Zfp329 1275 chr6 8900268 8900307 NM_008751 Nxph1 1289 chr18 37304447 37305772 NM_001003672 Pcdhac2 1325 chr1 20926262 20927612 NM_028829 Paqr8 1350 chr2 67955794 67957144 NM_020283 B3galt1 1350 chr15 100870221 100871571 NM_001077499 Scn8a 1350 chr5 82223313 82224688 NM_198702 Lphn3 1375 chr9 56466831 56468206 NM_181074 Lingo1 1375 chr18 46664659 46666043 NM_173423 Fem1c 1384 chr2 140485333 140486733 NM_001172160 Flrt3 1400 chr12 42179629 42181029 NM_010733 Lrrn3 1400 chr4 11887956 11889380 NM_001098231 Pdp1 1424 chr2 83719391 83720816 NM_175514 Fam171b 1425 chr9 111174998 111176523 NM_175266 Epm2aip1 1525 chr5 58111684 58113234 NM_001122758 Pcdh7 1550 chr10 112364133 112365733 NM_001033474 Atxn7l3b 1600 chr6 77193821 77195446 NM_028880 Lrrtm1 1625 chr15 80709571 80711271 NM_144812 Tnrc6b 1700 chr2 97469495 97471220 NM_178725 Lrrc4c 1725 chr11 117582888 117584738 NM_198022 Tnrc6c 1850 chr15 72636954 72640029 NR_002864 Peg13 3075

Shown in Table 4 are the 68 RNAs in mouse brain that have long (≧1 kb) stretches of contiguous m⁶A peaks. The genomic coordinates for each region of adjacent peaks are given (Chr; Start; End), as well as the RefSeq accession number and gene symbol for the transcript that contains each cluster of peaks. The distance that each set of contiguous peaks spans is also provided (Length Spanned). These sites of multiple contiguous m⁶A peaks are likely to represent regions of highly clustered m⁶A residues.

It was next determined which mRNAs contain m⁶A sites with the highest degree of methylation. To do this, a method of calculating the level of m⁶A enrichment at individual m⁶A peaks was developed, which normalized the number of MeRIP sample reads within each peak to the abundance of the individual transcript in which the peak resides (see Example 1, supra). The genes which contain the most enriched m⁶A peaks are listed in Table 5, infra. In particular, Table 5 shows the top twenty genes which contain m⁶A peaks with the highest levels of enrichment.

TABLE 5 Genes Encoding Transcripts with the Highest Degree of m6A Enrichment. Gene Enrichment Chr Peak Start Peak End RefSeq Accession Symbol Score chr6 58856032 58856214 NM_021432 Nap1l5 3.859 chr3 30717625 30717825 NM_027016 Sec62 3.693 chr3 88566067 88566234 NM_018804 Syt11 3.452 chr6 58855850 58856032 NM_021432 Nap1l5 3.327 chr19 5801367 5801534 NR_002847 Malat1 3.322 chr12 110898950 110899150 NR_028261 Rian 3.222 chr10 34018834 34018993 NM_030203 Tspyl4 3.162 chr17 6138575 6138775 NM_054040 Tulp4 2.984 chr2 34631357 34631514 NM_001163434 Hspa5 2.935 chr2 102630175 102630350 NM_001077514 Slc1a2 2.933 chr11 7072225 7072425 NM_009622 Adcy1 2.865 chr2 158211200 158211350 NM_175692 Snhg11 2.788 chr15 37326800 37327000 NM_134094 Ncald 2.685 chr2 102629600 102629788 NM_001077514 Slc1a2 2.672 chr15 37327000 37327200 NM_134094 Ncald 2.656 chr10 80644950 80645125 NM_007907 Eef2 2.451 chr2 102629788 102629975 NM_001077514 Slc1a2 2.392 chr11 59251613 59251750 NM_144521 Snap47 2.306 chr15 74581075 74581267 NM_011838 Lynx1 2.258 chr2 158211050 158211200 NM_175692 Snhg11 2.102

Importantly, because MeRIP-Seq identifies m⁶A sites at a resolution of 200 nt, there could be multiple individual m⁶A residues within the area covered by each peak. Therefore, the peaks with the highest levels of local m⁶A enrichment may represent a single adenosine residue which exhibits a high degree of methylation, or multiple adjacent m⁶A residues with a lower stoichiometry of methylation. In either case, however, the high levels of methylation observed at these sites likely indicate transcripts that are most influenced by m⁶A-dependent regulatory processes.

Example 8 m⁶A is Enriched Near Stop Codons and in 3′ UTRs of mRNAs

The distribution of m⁶A peaks within regions of the transcriptome in the high confidence set was next examined. The majority (94.8%) of m⁶A peaks occur within intragenic regions (FIG. 5C). These m⁶A peaks are abundant in coding sequences (CDS; 50.9%), and untranslated regions (UTRs; 41.9%), with relatively few in intronic regions (2.0%) (FIG. 5C). Additionally, m⁶A peaks are less abundant in the 5′ UTR (7.0% of UTR peaks) than in the 3′ UTR (93.0% of UTR peaks) (FIG. 5C). This distribution deviates substantially from the distribution of reads in the non-IP sample, indicating the high degree of enrichment of m⁶A peaks in the CDS and UTRs (FIG. 5C). Although a low percentage of m⁶A peaks was observed in intronic regions, because the samples were not enriched for unspliced pre-mRNAs, it is possible that additional methylated intronic sequences exist.

Next, it was sought to determine if m⁶A peaks are preferentially found in certain portions of transcripts. To do this, each m⁶A peak was assigned to either a 5′ UTR, CDS, or 3′ UTR category, and assigned it to one of 100 bins a based on its location along the 5′ UTR, CDS, or 3′ UTR. These data show that m⁶A occurs at low levels in the 5′ UTR and the 5′ end of the CDS. In the CDS, the percentage of m⁶A peaks increases steadily along transcript length and is on average 5-6 fold higher at the end of the CDS than at the beginning (FIG. 5D). In the 3′ UTR, the peaks are enriched near the stop codon and decrease in abundance along the length of the 3′ UTR. Indeed, 61% of m⁶A peaks are in the first quarter of the 3′ UTR and a quarter of all m⁶A peaks across the entire transcriptome are found within the first 26% of the 3′ UTR (FIG. 5D). Mapping the number of m⁶A peaks 1 kb up and downstream of CDS end sites further demonstrated the high levels of methylation in the vicinity of the stop codon (FIG. 9A, 9B). Collectively, these data indicate that m⁶A peaks are highly clustered in the vicinity of the stop codon in mRNAs.

Example 9 m⁶A Occurs in Highly Conserved Regions within Unique Sequence Motifs

Next, it was asked whether m⁶A sites are conserved across species. PhyloP scores were compared across 30 vertebrates (Pollard et al., “Detection of Nonneutral Substitution Rates on Mammalian Phylogenies,” Genome Res. 20:110-121 (2010), which is hereby incorporated by reference in its entirety) of m⁶A peak regions to those of random regions of the same size in gene exons. The distribution of conservation scores of the m⁶A peaks was significantly different from that of the random regions (p≦2.2×10⁻¹⁶, Kolmogorov-Smirnov (K-S) test, FIG. 6A) and m⁶A peaks' median conservation score (0.578) was much higher than that of the random regions (0.023). The fact that m⁶A frequently occurs in evolutionarily conserved sequences indicates that m⁶A-containing regions are maintained through selection pressure.

Because the tools for transcriptome-wide localization of m⁶A sites have until now been unavailable, only a few studies to date have examined the sequence contexts of m⁶A formation (Pollard et al., “Detection of Nonneutral Substitution Rates on Mammalian Phylogenies,” Genome Res. 20:110-121 (2010); Dimock et al., “Sequence Specificity of Internal Methylation in B77 Avian Sarcoma Virus RNA Subunits,” Biochemistry 16:471-478 (1977); Wei et al., “5′-Terminal and Internal Methylated Nucleotide Sequences in HeLa Cell mRNA,” Biochemistry 15:397-401 (1976), each of which is hereby incorporated by reference in its entirety). Using methods such as RNase T1 fingerprinting of radiolabeled RNA followed by separation by thin-layer chromatography, these studies reported that m⁶A exists within two unique sequence contexts: GAC and AAC (underlined adenosines indicate m⁶A). Subsequently, an extended m⁶A consensus sequence was identified: PuPuACX (Pu=purine; X=A, C, or U). However, since the methods used in these studies are not practical for use in a high-throughput manner, it is unclear whether these motifs are relevant to the transcriptome-wide m⁶A sites identified by MeRIP-Seq.

Therefore, the sequence motifs that are enriched within m⁶A peaks were sought to be identified. To do this, FIRE, a sensitive and unbiased tool for discovering RNA regulatory elements (Elemento et al., “A Universal Framework for Regulatory Element Discovery Across all Genomes and Data Types,” Mol. Cell 28:337-350 (2007), which is hereby incorporated by reference in its entirety) was used. Remarkably, FIRE independently identified the GAC and AAC motif, G[AG]ACU, and related variants ([AC]GAC[GU], GGAC, [AU][CG]G[AG]AC, and UGAC) as being highly enriched in m⁶A peaks (FIG. 6B). For example, the G[AG]ACU motif occurs in 42% of all m⁶A mRNA peaks and in a much lower fraction (21%) of non-m⁶A control peaks from the same mRNAs (p<1.0×10⁻¹²⁴, chi-square test). Altogether, it was found that >90% of all m⁶A peaks contain at least one of the motifs identified by FIRE.

Next, the position of the motifs within m⁶A peaks was examined. Nearly 30% of m⁶A peaks have only one motif (FIG. 8B), indicating that these peaks are likely to contain only a single methylated residue. Motifs are also preferentially found in the center of m⁶A peaks (FIG. 6C, 6D), indicating that these peaks derive from a centrally located methylated adenosine residue. Notably, other RNA regulatory elements, such as AU-rich elements, poly(A) signals, or binding sites for known RNA-binding proteins, were not identified by FIRE, indicating that m⁶A is unlikely to primarily function by modifying these known regulatory elements.

Example 10 Relationship Between m⁶A Sites and Polyadenylation Signals in 3′ UTRs

FIRE did not identify an enrichment of poly(A) signals (PASs), which are involved in 3′ UTR end processing, in m⁶A peaks. However, PASs exhibit considerable sequence heterogeneity beyond the canonical AAUAAA consensus (Tian et al., “A Large-Scale Analysis of mRNA Polyadenylation of Human and Mouse Genes,” Nucleic Acids Res. 33:201-212 (2005), which is hereby incorporated by reference in its entirety). This sequence heterogeneity might allow these PASs to evade detection by FIRE, despite being enriched in m⁶A peaks. Therefore, further investigation was sought to determine whether m⁶A peaks within 3′ UTRs are enriched at PASs. A high-confidence list was obtained (Brockman et al., “PACdb: PolyA Cleavage Site and 3′-UTR Database,” Bioinformatics 21:3691-3693 (2005), which is hereby incorporated by reference in its entirety) of poly(A) cleavage sites (the site downstream of a PAS where the mRNA is actually cleaved and polyadenylated) for the mRNAs that contain m⁶A peaks within their 3′ UTRs. Next, it was examined whether m⁶A peaks were enriched near these sites by determining the number of 3′ UTR m⁶A peaks that fell within 50 nt upstream of each cleavage site. Since a PAS is located approximately 10-30 nt upstream of an actual mRNA cleavage site (see Proudfoot, “Poly(A) Signals,” Cell 64:671-674 (1991), which is hereby incorporated by reference in its entirety), these 50 nt-long regions are expected to contain the PAS. Of the 6,288 m⁶A peaks found within 3′ UTRs, 1,042 (16.6%) overlapped with the 50 nt-long regions upstream of poly(A) cleavage sites, compared to 1,070 (17.0%) control peaks, which were generated from random nonoverlapping regions of the same 3′ UTRs. Thus, these data demonstrate that m⁶A do not have a significant association with known PASs (p=0.39; chi-square test).

Example 11 m⁶A is not Enriched at Splice Junctions

Prior studies that used nonspecific methylation inhibitors to explore possible functions for m⁶A revealed impaired splicing in a small number of RNAs (Carroll et al., “N6-Methyladenosine Residues in an Intron-Specific Region of Prolactin Pre-mRNA,” Mol. Cell Biol. 10:4456-4465 (1990); Stoltzfus et al., “Accumulation of Spliced Avian Retrovirus mRNA is Inhibited in S-Adenosylmethionine-Depleted Chicken Embryo Fibroblasts,” J. Virol. 42:918-931 (1982), each of which is hereby incorporated by reference in its entirety). Therefore, it was asked whether the localization of m⁶A peaks is compatible with a role for influencing the binding of splicing factors. However, only 80 splice junctions were found in regions contiguous with m⁶A peaks, significantly less than the overlap seen with a set of randomly-generated peaks (9,531; p=0.0; chi-square test). Thus, unlike CLIP-Seq tag clusters from RNA-binding proteins that influence splicing (Licatalosi et al., “HITS-CLIP Yields Genome-Wide Insights into Brain Alternative RNA Processing,” Nature 456:464-469 (2008), which is hereby incorporated by reference in its entirety), m⁶A peaks did not significantly coincide with exon-exon junctions, indicating that m⁶A is unlikely to primarily function to directly influence the binding of splicing factors.

Example 12 Relationship Between m⁶A and MicroRNA Binding Sites within 3′ UTRs

The strong enrichment of m⁶A peaks in 3′ UTRs prompted the investigation of whether m⁶A peaks are found near microRNA (miRNA) binding sites, which are also frequently observed within 3′ UTRs. It was found that 67% of 3′ UTRs that contain m⁶A peaks also contain at least one TargetScan-predicted miRNA binding site. Since ˜30% of genes have miRNA binding sites in their 3′ UTRs (Lewis et al., “Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets,” Cell 120:15-20 (2005), which is hereby incorporated by reference in its entirety), this is a significantly greater association than what would be expected by chance alone. Intriguingly, it was also found that in 3′ UTRs with both m⁶A peaks and miRNA binding sites, the m⁶A peaks precede miRNA binding sites 62% of the time. Moreover, it was found that the overall distribution of m⁶A peaks and miRNA binding sites within 3′ UTRs are anti-correlated; while m⁶A peaks are most abundant near the stop codon and generally decrease in frequency along 3′ UTR length, miRNA target sites are more enriched near the 3′ end of 3′ UTRs (FIG. 6E). The reason for this inverse localization pattern is unknown, although it could indicate that a certain spatial separation is necessary for m⁶A to influence the function of a downstream bound miRNA or vice versa.

Next, it was sought to determine whether miRNA-targeted transcripts in the brain are more likely to contain m⁶A. To test this, TargetScan was used to identify the target transcripts of the 25 most highly expressed and 25 least highly expressed miRNAs within the brain. Intriguingly, it was observed that the most highly expressed miRNAs have a significantly greater percentage of target transcripts that contain m⁶A (p<0.05, Wilcoxon test; FIG. 6F). These data indicate that miRNA levels may control methylation of their target transcripts.

Example 13 Prominent Features of m⁶A Distribution are Conserved in the Human Transcriptome

Next it was asked whether the enrichment of m⁶A in the 3′ UTR is also observed in other species. Therefore, m⁶A was profiled in HEK293T cells, a human cell line with high levels of adenosine methylation (FIG. 3B). A high-confidence list of m⁶A peaks was generated using three MeRIP-Seq biological replicates and confirmed by both m⁶A antibodies. It was found that the distribution of m⁶A peaks in HEK293T cells closely mirrored the distribution in mouse brain, with 31% and 53% of m⁶A peaks falling within the 3′ UTR and the CDS, respectively (FIG. 5D, FIG. 9D). As with the pattern of m⁶A distribution in the mouse brain transcriptome, HEK293T m⁶A peaks were predominantly localized near stop codons (FIG. 5D and FIG. 9C).

In total, 18,756 peaks were identified in RNAs encoded by 5,768 genes in HEK293T cells. Additionally, transcripts were found from 2,145 and 3,259 genes and were methylated only in the mouse brain and HEK293T datasets, respectively, and transcripts from 2,509 genes were methylated in both datasets. Interestingly, among the transcripts methylated in both tissues, m⁶A peaks were often localized to the same distinct regions of both orthologs (FIG. 5E). Collectively, these data indicated that m⁶A peaks are enriched near the stop codon in human transcripts and that many sites of methylation are conserved in mouse and human transcripts.

Discussion of Examples 1-13

Unlike DNA, which undergoes cytosine methylation and hydroxymethylation, dynamic internal modifications of mRNA other than RNA editing have not been established. Recent evidence that the obesity risk gene, FTO, is a physiologic m⁶A demethylase suggests that m⁶A has central roles in cellular function. Here, MeRIP-Seq (the method of the present invention) is used to provide the first transcriptome-wide characterization of m⁶A. It is shown that m⁶A is a reversible and widespread modification which is primarily located in evolutionarily conserved regions and is particularly enriched near the stop codon. It was found that many features of m⁶A localization are conserved between the human and mouse transcriptomes, and a previously unidentified link between m⁶A and miRNA signaling was uncovered. Collectively, these studies reveal that m⁶A is a widespread and dynamically regulated base modification in mRNA, and they identify mRNAs which are most likely to be influenced by signaling pathways that influence m⁶A levels.

One of the most striking features of m⁶A localization is its prevalence within 3′ UTRs. The 3′ UTR is an important region for RNA regulation, as it can influence RNA stability, subcellular localization, and translation regulation. Several of these events are regulated by RNA binding proteins (RBPs) that bind to cis-acting structural motifs or consensus sequences within the 3′ UTR and act to coordinate RNA processing. Conceivably, m⁶A may influence the affinity of specific RBPs for their target mRNAs, analogous to the recruitment of methyl-CpG binding protein 2 (MeCP2) to methylated cytosine residues in DNA (Lewis et al., “Purification, Sequence, and Cellular Localization of a Novel Chromosomal Protein that Binds to Methylated DNA,” Cell 69:905-914 (1992), which is hereby incorporated by reference in its entirety). Given the abundance of m⁶A throughout the transcriptome and its widespread localization, such a role for m⁶A would be likely to have important consequences for the regulation of numerous mRNAs.

The profiling of m⁶A in HEK293T cells in the present invention revealed thousands of transcripts that are also methylated in the mouse brain. In many cases, the patterns of m⁶A localization within these transcripts are nearly identical, suggesting that some RNAs possess highly conserved methylation profiles. However, many transcripts were also uncovered that exhibit distinct cell type-specific methylation patterns, demonstrating that m⁶A is also capable of being differentially regulated within unique cellular environments.

The finding of the present invention that a large proportion of 3′ UTRs that contain m⁶A peaks also contain miRNA binding sites is highly suggestive of an association between m⁶A and miRNA function. Additionally, this analysis also indicated an inverse localization of m⁶A peaks and miRNA binding sites within 3′ UTRs, with m⁶A sites typically preceding, but not overlapping, the miRNA sites in the 3′ UTRs. Although miRNAs can inhibit their target mRNAs by promoting either transcript degradation or translational repression (Guo et al., “Mammalian MicroRNAs Predominantly Act to Decrease Target mRNA Levels,” Nature 466:835-840 (2010); Hendrickson et al., “Concordant Regulation of Translation and mRNA Abundance for Hundreds of Targets of a Human MicroRNA,” PLoS Biol 7(11):e1000238 (2009), each of which is hereby incorporated by reference in its entirety), the factors that determine which fate predominates are not well understood. Conceivably, the proximity of m⁶A to a miRNA binding site could influence the mechanism of miRNA-mediated transcript inhibition. Additionally, it is possible that miRNA binding influences m⁶A levels within 3′ UTRs. Indeed, the finding of the present invention that abundant miRNAs are more significantly enriched in m⁶A peaks than weakly expressed miRNAs raises the possibility that miRNAs regulate methylation status.

A surprising result of these studies is the finding that m⁶A is highly enriched near stop codons. This recurrent localization within transcripts indicates that adenosine methylation in the vicinity of the stop codon may be of functional importance. Interestingly, the consensus for adenosine methylation is relatively short, and sequences that match the consensus are found throughout the transcriptome. However, despite the frequency of m⁶A consensus sites, methylation occurs primarily near stop codons.

The finding that FTO demethylates m⁶A suggests that misregulation of pathways controlled by adenosine methylation ultimately affect physiologic processes in humans. Although m⁶A is found in many classes of RNA, it is intriguing to speculate that FTO mutations mediate their effects by affected m⁶A in mRNA. Indeed, the present finding that FTO can demethylate diverse mRNAs is consistent with this model.

The present invention demonstrates that m⁶A is a widespread modification found in a large fraction of cellular mRNA. The pervasive nature of this modification indicates that adenosine methylation has important roles in RNA biology. Much how cytosine methylation and hydroxymethylation in DNA are important epigenetic regulators of the genome, the present data demonstrate that adenosine methylation in RNA is a reversible modification that is likely to influence a wide variety of biological pathways and physiological processes.

Although the invention has been described in detail for the purpose of illustration, it is understood that such detail is solely for that purpose, and variations can be made therein by those skilled in the art without departing from the spirit and scope of the invention which is defined by the following claims. 

1. A method of characterizing the modified base status of a transcriptome, said method comprising: contacting a transcriptome comprising one or more modified bases with an antibody specific to the one or more modified bases under conditions effective to bind the antibody to the one or more modified bases; isolating, from the transcriptome, a pool of RNA transcripts to which the antibody binds; and identifying isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome, wherein each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome.
 2. The method according to claim 1, wherein the antibody is coupled to a magnetic bead or a paramagnetic bead.
 3. The method according to claim 1, wherein said isolating is carried out by immunoprecipitation.
 4. The method according to claim 1 further comprising: fragmenting RNA transcripts of the transcriptome before said contacting to form RNA transcript fragments, wherein the isolated RNA transcripts are RNA transcript fragments.
 5. The method according to claim 4, wherein said RNA transcript fragments are about 200 nucleotides in length, 150 nucleotides in length, 100 nucleotides in length, 50 nucleotides in length, 25 nucleotides in length, 10 nucleotides in length, or any combination thereof.
 6. The method according to claim 4 further comprising: sequencing the RNA transcript fragments and isolated RNA transcript fragments.
 7. The method according to claim 4 further comprising: measuring the abundance of both the RNA transcript fragments of the transcriptome and the isolated RNA transcript fragments.
 8. The method according to claim 4, wherein said identifying comprises comparing the abundance of an RNA transcript fragment in the isolated pool to the abundance of that RNA transcript fragment in the transcriptome prior to said isolating.
 9. The method according to claim 1, wherein the modified base is a substituted base.
 10. The method according to claim 1, wherein the modified base is a methylated base.
 11. The method according to claim 10, wherein the one or more modified bases is selected from m⁶A, m⁵C, hm⁵C, and inosine.
 12. The method according to claim 1 further comprising: obtaining a transcriptome from a cell or a tissue.
 13. The method according to claim 12, wherein said obtaining comprises polysome fractionation, poly(A) purification, exome capture, ribo-depletion, size fractionation, phenol-chloroform extraction, 70% ethanol elution, 100% ethanol elution, microRNA/small RNA isolation, or isolation of any RNA type.
 14. The method according to claim 12, wherein said obtaining is from a single cell type.
 15. The method according to claim 12, wherein said obtaining is from a single tissue type.
 16. The method according to claim 12, wherein said obtaining is from a diseased tissue.
 17. The method according to claim 12, wherein said obtaining is from a non-diseased tissue.
 18. The method according to claim 12, wherein said obtaining is from a diseased cell.
 19. The method according to claim 12, wherein said obtaining is from a non-diseased cell.
 20. The method according to claim 1 further comprising: comparing the modified base status of a transcriptome from a first cell type or tissue type to the modified base status of a transcriptome from a second cell type or tissue type.
 21. The method according to claim 20, wherein the first cell type or tissue type is from a non-diseased cell or tissue and the second cell type or tissue type is from a diseased cell or tissue.
 22. A method of diagnosis or prognosis of a disease or disorder associated with a modified base in RNA in a subject, said method comprising: obtaining a transcriptome from a subject, wherein the transcriptome comprises one or more modified bases; characterizing the modified base status of the transcriptome; and comparing the modified base status of the transcriptome to a known modified base status of a comparable transcriptome from a healthy and/or diseased subject to provide a diagnosis or prognosis of a disease or disorder in the subject.
 23. The method according to claim 22, wherein said characterizing comprises: contacting the transcriptome with an antibody specific to the one or more modified bases under conditions effective to bind the antibody to the one or more modified bases; isolating, from the transcriptome, a pool of RNA transcripts to which the antibody binds; and identifying isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome, wherein each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome.
 24. The method according to claim 22, wherein the modified base is a substituted base.
 25. The method according to claim 22, wherein the modified base is a methylated base.
 26. The method according to claim 25, wherein the one or more modified bases is selected from m⁶A, m⁵C, hm⁵C, and inosine.
 27. The method according to claim 22, wherein said obtaining is from a cell or a tissue.
 28. The method according to claim 27, wherein said obtaining comprises polysome fractionation, poly(A) purification, exome capture, ribo-depletion, size fractionation, phenol-chloroform extraction, 70% ethanol elution, 100% ethanol elution, microRNA/small RNA isolation, or isolation of any RNA type.
 29. The method according to claim 27, wherein said obtaining is from a single cell type.
 30. The method according to claim 27, wherein said obtaining is from a single tissue type.
 31. The method according to claim 27, wherein said obtaining is from a diseased tissue.
 32. The method according to claim 27, wherein said obtaining is from a non-diseased tissue.
 33. The method according to claim 27, wherein said obtaining is from a diseased cell.
 34. The method according to claim 27, wherein said obtaining is from a non-diseased cell.
 35. A method of determining the effect of a treatment on modified base levels of a transcriptome from a subject, said method comprising: characterizing the modified base status of a transcriptome comprising one or more modified bases from a subject before a treatment is administered to the subject; characterizing the modified base status of the transcriptome from the subject after a treatment is administered to the subject; and comparing the modified base status of the transcriptome after said treatment to the modified base status of the transcriptome before said treatment to determine the effect of the treatment on modified base levels of the transcriptome in the subject.
 36. The method according to claim 35, wherein said characterizing comprises: contacting the transcriptome with an antibody specific to the one or more modified bases under conditions effective to bind the antibody to the one or more modified bases; isolating, from the transcriptome, a pool of RNA transcripts to which the antibody binds; and identifying isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome, wherein each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome.
 37. The method according to claim 35, wherein the modified base is a substituted base.
 38. The method according to claim 35, wherein the modified base is a methylated base.
 39. The method according to claim 38, wherein the one or more modified bases is selected from m⁶A, m⁵C, hm⁵C, and inosine.
 40. The method according to claim 35 further comprising: obtaining a transcriptome from a cell sample or a tissue sample.
 41. The method according to claim 40, wherein said obtaining comprises polysome fractionation, poly(A) purification, exome capture, ribo-depletion, size fractionation, phenol-chloroform extraction, 70% ethanol elution, 100% ethanol elution, microRNA/small RNA isolation, or isolation of any RNA type.
 42. The method according to claim 40, wherein said obtaining is from a single cell type.
 43. The method according to claim 40, wherein said obtaining is from a single tissue type.
 44. The method according to claim 40, wherein said obtaining is from a diseased tissue.
 45. The method according to claim 40, wherein said obtaining is from a non-diseased tissue.
 46. The method according to claim 40, wherein said obtaining is from a diseased cell.
 47. The method according to claim 40, wherein said obtaining is from a non-diseased cell.
 48. A kit for characterizing the modified base status of a transcriptome, said kit comprising: an antibody that binds a modified base in RNA; one or more buffer solutions for carrying out antibody binding to a modified base in RNA, isolation of antibody-bound RNA, and/or sequencing of RNA molecules; and a computer-readable medium having stored thereon computer-readable instructions for comparing the abundance of an RNA transcript fragment in an isolated pool of RNA transcripts from a transcriptome to the abundance of that RNA transcript in the transcriptome prior to isolation thereof to identify isolated RNA transcripts that are present in a higher abundance in the isolated pool relative to the transcriptome, wherein each of said isolated RNA transcripts that are present in a higher abundance in the isolated pool together characterize the modified base status of the transcriptome.
 49. The kit according to claim 48, wherein the antibody is selected from an anti-m⁶A antibody, an anti-m⁵C antibody, an anti-hm⁵C antibody, and anti-inosine antibody. 